Estimated Regression Coefficients Research Articles

Effect of Physical Resource Allocation Strategies on Public Service Delivery: A Case of Vihiga County Government, Kenya

Resource allocation strategies are important components of the strategic management process. They are applied by organizations during the implementation of a formulated strategy so as to achieve competitive advantage in markets. The purpose of this study was to determine how strategies for allocating physical resources affect the delivery of public services in Vihiga County. Asset-based views and systems theories were embraced to help the review. During the course of the investigation, a descriptive survey research design was used. A target population of 593 individuals was used, and a sample size of 234 respondents was picked in view of Krecjie and Morgan's recipe. The respondents were distributed among the participating institutions, and each participant was selected at random. Respondents were selected from health and Early Childhood Development (ECD) facilities, primary and secondary schools, trade, infrastructure, and public service departments to fill out questionnaires. The researcher conducted in-person interviews with selected administrators as well as document analysis within the finance department. Descriptive statistics like frequencies, percentages, and means were used to collect and analyze the data using both qualitative and quantitative methods. Using SPSS, quantitative data were analyzed to determine how strong the relationship was between the variables and the results shown in tables, graphs, and charts. The regression model's estimates of the regression show that strategies for allocating physical resources have a significant impact on the delivery of public services. The estimates of the significant regression coefficient are r = 0.649, t = 7.819, and the p-value is 0.01. The coefficient estimate's P-value is less than 0.05, indicating significance at the 95% confidence level. According to this significant estimate, the levels of the public service delivery index would rise by 0.649 units if physical resource allocation strategies were increased by one unit in county government. The findings made it possible to draw the conclusion that the Vihiga County government's resource allocation strategies and public service delivery have a positive and significant relationship and that government regulation has a positive and significant effect on moderating the relationship. The study recommends that adequate financing of technological resources should be done at all times to enable efficient monitoring of service provision. The study becomes reference material for all county government staff, policymakers, and other institutions in Kenya.

Multiple imputation of more than one environmental exposure with nondifferential measurement error.

Measurement error is common in environmental epidemiologic studies, but methods for correcting measurement error in regression models with multiple environmental exposures as covariates have not been well investigated. We consider a multiple imputation approach, combining external or internal calibration samples that contain information on both true and error-prone exposures with the main study data of multiple exposures measured with error. We propose a constrained chained equations multiple imputation (CEMI) algorithm that places constraints on the imputation model parameters in the chained equations imputation based on the assumptions of strong nondifferential measurement error. We also extend the constrained CEMI method to accommodate nondetects in the error-prone exposures in the main study data. We estimate the variance of the regression coefficients using the bootstrap with two imputations of each bootstrapped sample. The constrained CEMI method is shown by simulations to outperform existing methods, namely the method that ignores measurement error, classical calibration, and regression prediction, yielding estimated regression coefficients with smaller bias and confidence intervals with coverage close to the nominal level. We apply the proposed method to the Neighborhood Asthma and Allergy Study to investigate the associations between the concentrations of multiple indoor allergens and the fractional exhaled nitric oxide level among asthmatic children in New York City. The constrained CEMI method can be implemented by imposing constraints on the imputation matrix using the mice and bootImpute packages in R.

Associations between physical and mental health and the utilization of ambulatory and emergency healthcare among asylum-seekers: results from a cross-sectional survey in Berlin, Germany

BackgroundDespite a high burden of chronic and mental illness, asylum-seekers show low utilization of ambulatory specialist healthcare. Forgoing timely healthcare when facing access barriers may direct them toward emergency care. This paper examines interrelations of physical and mental health and utilization of ambulatory and emergency care, and explicitly addresses associations between the different types of care.MethodsA structural equation model was applied to a sample of n = 136 asylum-seekers living in accommodation centers in Berlin, Germany. Utilization patterns of emergency care (outcome) and physical and mental ambulatory care (endogenous predictors) were estimated, while controlling for age, gender, chronic conditions, bodily pain, depression, anxiety, length of stay in Germany (exogenous predictors) and self-rated health (endogenous predictor).ResultsAssociations were observed between ambulatory care utilization and poor self-rated health (0.207, CI: 0.05; 0.364), chronic illness (0.096, CI: 0.017; 0.175) and bodily pain (0.019, CI: 0.002; 0.036); between mental healthcare utilization and anxiety (0.202, CI: 0.051; 0.352); and between emergency care utilization and poor self-rated health (0.621, CI: 0.059; 1.183), chronic illness (0.287, CI: 0.012; 0.563), mental healthcare utilization (0.842, CI: 0.148; 1.535) and anxiety (0.790, CI: 0.141; 1.438) (values in parentheses show estimated regression coefficients and 95% confidence intervals). We found no associations between the utilization of ambulatory and emergency care.ConclusionsOur study generates mixed results concerning associations between healthcare needs and ambulatory and emergency care utilization among asylum-seekers. We found no evidence that low utilization of ambulatory care contributes to emergency care utilization; neither did we find evidence that ambulatory treatment obviates the need to seek emergency care. Our results indicate that higher physical healthcare needs and anxiety are associated with more utilization of both ambulatory and emergency care; whereas healthcare needs related to depression tend to remain unmet. Both the undirected and under-utilization of health services may reflect navigation and accessibility issues. To facilitate more needs-based and effective healthcare utilization and thus contribute to health equity, support services such as interpretation and care navigation as well as outreach are warranted.

LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data.

From a systematic perspective, it is crucial to infer and analyze gene regulatory network (GRN) from high-throughput single-cell RNA sequencing data. However, most existing GRN inference methods mainly focus on the network topology, only few of them consider how to explicitly describe the updated logic rules of regulation in GRNs to obtain their dynamics. Moreover, some inference methods also fail to deal with the over-fitting problem caused by the noise in time series data. In this article, we propose a novel embedded Boolean threshold network method called LogBTF, which effectively infers GRN by integrating regularized logistic regression and Boolean threshold function. First, the continuous gene expression values are converted into Boolean values and the elastic net regression model is adopted to fit the binarized time series data. Then, the estimated regression coefficients are applied to represent the unknown Boolean threshold function of the candidate Boolean threshold network as the dynamical equations. To overcome the multi-collinearity and over-fitting problems, a new and effective approach is designed to optimize the network topology by adding a perturbation design matrix to the input data and thereafter setting sufficiently small elements of the output coefficient vector to zeros. In addition, the cross-validation procedure is implemented into the Boolean threshold network model framework to strengthen the inference capability. Finally, extensive experiments on one simulated Boolean value dataset, dozens of simulation datasets, and three real single-cell RNA sequencing datasets demonstrate that the LogBTF method can infer GRNs from time series data more accurately than some other alternative methods for GRN inference. The source data and code are available at https://github.com/zpliulab/LogBTF.

MP64-19 IMPACT OF NUMBER OF POSITIVE LYMPH NODES ON PROGNOSTIC STRATIFICATION IN RENAL CELL CARCINOMA: ANALYSIS OF THE NATIONAL CANCER DATABASE

You have accessJournal of UrologyCME1 Apr 2023MP64-19 IMPACT OF NUMBER OF POSITIVE LYMPH NODES ON PROGNOSTIC STRATIFICATION IN RENAL CELL CARCINOMA: ANALYSIS OF THE NATIONAL CANCER DATABASE Luke Wang, Florin Vaida, Dhruv Puri, Sohail Dhanji, Franklin Liu, Jonathan Afari, Margaret Meagher, Kevin Hakimi, Mimi Nguyen, Cesare Saitta, Saeed Ghassemzadeh, Aastha Shah, Ryan Nasseri, Juan Javier-Desloges, James Murphy Mckay, and Ithaar Derweesh Luke WangLuke Wang More articles by this author , Florin VaidaFlorin Vaida More articles by this author , Dhruv PuriDhruv Puri More articles by this author , Sohail DhanjiSohail Dhanji More articles by this author , Franklin LiuFranklin Liu More articles by this author , Jonathan AfariJonathan Afari More articles by this author , Margaret MeagherMargaret Meagher More articles by this author , Kevin HakimiKevin Hakimi More articles by this author , Mimi NguyenMimi Nguyen More articles by this author , Cesare SaittaCesare Saitta More articles by this author , Saeed GhassemzadehSaeed Ghassemzadeh More articles by this author , Aastha ShahAastha Shah More articles by this author , Ryan NasseriRyan Nasseri More articles by this author , Juan Javier-DeslogesJuan Javier-Desloges More articles by this author , James Murphy MckayJames Murphy Mckay More articles by this author , and Ithaar DerweeshIthaar Derweesh More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003322.19AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The prognostic significance of lymph node (LN) positivity in renal cell carcinoma (RCC) is poorly understood. Currently, American Joint Committee on Cancer (AJCC) Stage III RCC includes both node-positive pN1 and node-negative pN0 disease. We hypothesize that (1) there is a threshold in number of pathologic positive LN (pLN) that distinguishes favorable from poor risk nodal disease, and (2) current categorization of pN1 can be subdivided into pN1 and pN2 based this threshold. We tested our hypothesis using the National Cancer Database (NCDB). METHODS: From 2004-2019, all RCC were queried in patients age ≥18. pN1 RCC with no synchronous metastasis were selected to minimize confounding from metastatic burden. Multivariable Cox regression tested association between number of pLN and all-cause mortality (ACM). Receiver Operator Characteristic (ROC) Curve analyses employing concordance probability method and analysis of regression coefficients evaluated performance of potential cut-points for pN2. Kaplan-Meier analyses (KMA) compared these thresholds against overall survival (OS) in non-metastatic Stage IV RCC. RESULTS: 29,388 patients were identified; 3910 (13.3%) had pN1. On Cox regression, increased number of pLN was associated with increased hazard of ACM (HR 1.19, p<0.001). Analysis of estimated regression coefficients identified potential threshold at ≥3 pLN. ROC mapping of thresholds ≥2 to ≥10 showed stable concordance probability 0.26-0.33, AUC=0.656. On KMA, when threshold was ≥3, 5-year OS was no longer significantly different from non- metastatic Stage IV RCC as illustrated by overlapping confidence intervals. We designated pN1 as 1-2 pLN, and pN2 as ≥3 pLN. 5-year OS for Stage III pN0 was 69.4% (95% CI 68.4-70.5), for Stage III pN1 was 41.4% (39.0-43.8), for Stage III pN2 was 31.8% (28.2-35.9%), and for non-metastatic Stage IV was 30.0% (28.2-32.0%). pN2 exhibited 38% greater hazard of ACM (HR 3.31, p<0.001) compared to pN1 (HR 2.39, p<0.001). CONCLUSIONS: These findings show that original pN1 RCC could be stratified to pN1 and pN2, with pN2 conferring poor prognosis comparable to non-metastatic Stage IV pN0 disease. Although validation studies are warranted, consideration should be given towards stratifying Stage III pN2 patients to a higher risk group. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e888 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Luke Wang More articles by this author Florin Vaida More articles by this author Dhruv Puri More articles by this author Sohail Dhanji More articles by this author Franklin Liu More articles by this author Jonathan Afari More articles by this author Margaret Meagher More articles by this author Kevin Hakimi More articles by this author Mimi Nguyen More articles by this author Cesare Saitta More articles by this author Saeed Ghassemzadeh More articles by this author Aastha Shah More articles by this author Ryan Nasseri More articles by this author Juan Javier-Desloges More articles by this author James Murphy Mckay More articles by this author Ithaar Derweesh More articles by this author Expand All Advertisement PDF downloadLoading ...

A multiple imputation-based sensitivity analysis approachfor regression analysis with a missing notatrandom covariate.

Missing covariate problems are common in biomedical and electrical medical record data studies while evaluating the relationship between a biomarker and certain clinical outcome, when biomarker data are not collected for all subjects. However, missingness mechanism is unverifiable based on observed data. If there is a suspicion of missing not at random (MNAR), researchers often perform sensitivity analysis to evaluate the impact of various missingness mechanisms. Under the selection modeling framework, we propose a sensitivity analysis approach with a standardized sensitivity parameter using a nonparametric multiple imputation strategy. The proposed approach requires fitting two working models to derive two predictive scores: one for predicting missing covariate values and the other for predicting missingness probabilities. For each missing covariate observation, the two predictive scores along with the pre-specified sensitivity parameter are used to define an imputing set. The proposed approach is expected to be robust against mis-specifications of the selection model and the sensitivity parameter since the selection model and the sensitivity parameter are not directly used to impute missing covariate values. A simulation study is conducted to study the performance of the proposed approach when MNAR is induced by Heckman's selection model. Simulation results show the proposed approach can produce plausible regression coefficient estimates. The proposed sensitivity analysis approach is also applied to evaluate the impact of MNAR on the relationship between post-operative outcomes and incomplete pre-operative Hemoglobin A1c level for patients who underwent carotid intervetion for advanced atherosclerotic disease.

Inequality Dynamics in Urban Manipur, India: A Decomposition Analysis.

The high pace of urbanization in Manipur being confined to the valley regions has attracted much attention to researchers to explore the intra-state dynamics of urban inequality in the state. This study examines the role of spatial factors in determining consumption inequality in the state, especially in urban areas considering the unit-level National Sample Survey data from different rounds. Also, the Regression-Based Inequality Decomposition is estimated to understand the role played by relevant household characteristics in explaining the inequality patterns in urban Manipur. The study observes an increasing trend of Gini coefficient for the overall state despite its slow per-capita growth. While Gini measures of consumption have an overall increasing trend in the economy between 1993 and 2011, and inequality level in rural areas is greater than in urban areas in 2011-2012. This is in contrast to the overall Indian phenomenon. Also, the state per capita income is 43% lower than the all-India average in 2019-2020 at 2011-2012 prices. Within-component (both within-district and within-sector) is the major contributor to overall consumption inequality. The decomposition-based regression analysis shows that most of the estimated regression coefficients are statistically significant. Factors like age, possession of land, and regular salary earner in a household contribute to enhancing the level of total inequality of the average MPCE. This paper suggests that in order to avoid the negative consequences of rising consumption inequality in Manipur, a justiciable land redistribution policy, improving the level of education, and creating employment opportunities are necessary.

Integrating Data Across Misaligned Spatial Units

AbstractTheoretical units of interest often do not align with the spatial units at which data are available. This problem is pervasive in political science, particularly in subnational empirical research that requires integrating data across incompatible geographic units (e.g., administrative areas, electoral constituencies, and grid cells). Overcoming this challenge requires researchers not only to align the scale of empirical and theoretical units, but also to understand the consequences of this change of support for measurement error and statistical inference. We show how the accuracy of transformed values and the estimation of regression coefficients depend on the degree of nesting (i.e., whether units fall completely and neatly inside each other) and on the relative scale of source and destination units (i.e., aggregation, disaggregation, and hybrid). We introduce simple, nonparametric measures of relative nesting and scale, asex anteindicators of spatial transformation complexity and error susceptibility. Using election data and Monte Carlo simulations, we show that these measures are strongly predictive of transformation quality across multiple change-of-support methods. We propose several validation procedures and provide open-source software to make transformation options more accessible, customizable, and intuitive.

Challenges of food security in the Gulf Cooperation Council countries: an empirical analysis of fixed and random effects

Purpose. This research aims to identify the challenges of the food security indicators in the Gulf Cooperation Council (GCC) countries for the period 2000–2020.&#x0D; Methodology / approach. The dataset of this study includes the annual secondary data covering the time 2000–2019 of the six GCC countries. The dependents variables represent the food security indicators whereas, the independents variables (explanatory) represent the challenging factors of the food security at the macro-level data. All the studied variables are reshaped in the balanced panel form; thus, the study uses a long panel, it has many periods (T = 20 years) but few entities (n = 6 countries of GCC) with the total 120 observation. The study applies three alternatives to panel data analysis. &#x0D; Results. The results show that population is a significant driver of food security challenges in the GCC countries. Moreover, food price inflation has a significant impact on the food availability and stability but show no significance on food access and utilization. Fertilizer consumption causes significant problems with food use. The results show that there is an evidence of significant differences in food security across the GCC countries. The random-effects estimators of regression coefficients of food availability and stability challenges are more statistically efficient than those for pooled ordinary least square and fixed effects. While the fixed effects estimators are most preferred for the coefficients of food access and utilization challenges.&#x0D; Originality / scientific novelty. Most scientific articles are mostly focused on examining the food security challenges from one separate aspect: economics, climate, or social aspects. Thus, the scientific novelty of the study is to investigate the combination of the food security challenges, social, economic, and agri-environmental factors in the GCC oil countries. &#x0D; Practical value / implications. Through this research, it is proposed that decision-makers embark on interferences that stimulate food security to meet the continual increase in population also future research should be applied to the other factors that challenged food security.

Minimum profile hellinger distance estimation of semiparametric multiple linear regression models

As the most fundamental tool to analyse associations, the semiparametric multiple linear regression model is considered in this work where the error distribution is assumed symmetric but otherwise completely unknown. To handle the common concern about possible outlying observations, especially in the era of big data, we propose a robust estimator of regression coefficients based on Hellinger distance and profiling technique. We prove in theory that the resulting estimator, minimum profile Hellinger distance estimator (MPHDE), is consistent. Its finite-sample performance is examined via both extensive simulation studies and a real data application. Our numerical results demonstrate that the proposed MPHDE has good efficiency and simultaneously is very robust against outlying observations.

The Effects of Omitting Components in a Multilevel Model With Social Network Effects

Multilevel models are often used to account for the hierarchical structure of social data and the inherent dependencies to produce estimates of regression coefficients, variance components associated with each level, and accurate standard errors. Social network analysis is another important approach to analysing complex data that incoproate the social relationships between a number of individuals. Extended linear regression models, such as network autoregressive models, have been proposed that include the social network information to account for the dependencies between persons. In this article, we propose three types of models that account for both the multilevel structure and the social network structure together, leading to network autoregressive multilevel models. We investigate theoretically and empirically, using simulated data and a data set from the Dutch Social Behavior study, the effect of omitting the levels and the social network on the estimates of the regression coefficients, variance components, network autocorrelation parameter, and standard errors.

Spatial Linear Regression with Covariate Measurement Errors: Inference and Scalable Computation in a Functional Modeling Approach

For spatial linear models, the classical maximum-likelihood estimators of both regression coefficients and variance components can be biased when the covariates are measured with errors. This work introduces a theoretically backed-up estimation framework for the spatial linear errors-in-variables model in a functional approach. Compared with the structural models, the functional approach treats the unobserved true covariates as fixed unknown parameters without imposing additional structures, thus leading to more robust parameter inference. Our model parameters are estimated simultaneously based on a set of unbiased estimating equations. Under some regularity conditions, we prove the consistency of the proposed estimating-equation estimators and derive their asymptotic distribution. In addition, a consistent variance estimator is developed for the estimating-equation estimators. To handle large spatial datasets, we provide two approaches to obtain scalable estimations based on our proposed estimating equations, where the required computational time and storage are reduced to be linear with sample size for each estimating-function evaluation. Simulation studies under different settings show that our estimators are consistent and the scalable algorithms work well. Finally, the proposed method is applied to studying the relationship between Arctic sea ice and related geophysical variables. Supplementary materials for this article are available online.

Implicates as Instrumental Variables: An Approach for Estimation and Inference with Probabilistically Matched Data

Abstract Linkage errors in probabilistically matched data sets can cause biases in the estimation of regression coefficients. This article proposes an approach to obtain consistent estimates and valid inference that relies on instrumental variables. The novelty of the method is to show that instrumental variables arise naturally in the course of probabilistic record linkage thereby allowing for off-the-shelf implementation. Relative to existing approaches, the instrumental variable approach does not require integration of the record linkage and regression analysis steps, the estimation of complex models of linkage error, or computationally expensive methods to estimate standard errors. The instrumental variables approach performs well in Monte Carlo simulations of an environment highlighting a many-to-one linkage problem.

A synthetic data integration framework to leverage external summary-level information from heterogeneous populations.

There is a growing need for flexible general frameworks that integrate individual-level data with external summary information for improved statistical inference. External information relevant for a risk prediction model may come in multiple forms, through regression coefficient estimates or predicted values of the outcome variable. Different external models may use different sets of predictors and the algorithm they used to predict the outcome Y given these predictors may or may not be known. The underlying populations corresponding to each external model may be different from each other and from the internal study population. Motivated by a prostate cancer risk prediction problem where novel biomarkers are measured only in the internal study, this paper proposes an imputation-based methodology, where the goal is to fit a target regression model with all available predictors in the internal study while utilizing summary information from external models that may have used only a subset of the predictors. The method allows for heterogeneity of covariate effects across the external populations. The proposed approach generates synthetic outcome data in each external population, uses stacked multiple imputation to create a long dataset with complete covariate information. The final analysis of the stacked imputed data is conducted by weighted regression. This flexible and unified approach can improve statistical efficiency of the estimated coefficients in the internal study, improve predictions by utilizing even partial information available from models that use a subset of the full set of covariates used in the internal study, and provide statistical inference for the external population with potentially different covariate effects from the internalpopulation.

Influencing factors of depressive symptoms among undergraduates: A systematic review and meta-analysis.

This systematic review aims to examine the influencing factors of undergraduates' depressive symptoms by summarizing the categories and intensity of the factors, to lay a foundation for subsequent research. Two authors independently searched in Medline (Ovid), Embase (Ovid), Scopu, PsycINFO, PsycARTICLES, the Chinese Scientific Journal Database (VIP Database), China National Knowledge database (CNKI), and the WanFang database for cohort studies related to the influencing factors affecting depressive symptoms among undergraduates published prior to September 12, 2022. Adjusted Newcastle-Ottawa scale (NOS) was used to assess the risk of bias. Meta-analyses of regression coefficient estimates were performed to calculate pooled estimates with R 4.0.3 software. A total of 73 cohort studies were included, involving 46362 participants from 11 countries. Factors affecting depressive symptoms were classified into relational, psychological, predictors of response to trauma, occupational, sociodemographic and lifestyle factors. In Meta-analysis, 4 of 7 influencing factors were revealed to be statistically significant: negative coping (B = 0.98, 95%CI: 0.22-1.74), rumination (B = 0.06, 95%CI: 0.01-0.11), stress (OR = 0.22, 95%CI: 0.16-0.28) and childhood abuse (B = 0.42, 95%CI:0.13-0.71). No significant association was found in positive coping, gender and ethnicity. The current studies have the problems of inconsistent use of scales and large heterogeneity of research design, making it difficult to summarize, which is expected to be further improved in future research. This review evidences the importance of several influencing factors of depressive symptoms among undergraduates. We advocate for more high-quality studies with more coherent and appropriate study designs and outcome measurement approaches in this field. Systematic review registration: PROSPERO registration CRD42021267841.

A 30-DAY RISK ASSESSMENT OF GERIATRIC PATIENTS IN THE EMERGENCY DEPARTMENT: A COMPARISON OF ISAR AND TRST SCORES

Introduction: The aim of this prospective observational study was to compare the predictive ability of the Triage Risk Stratification Tool and Identification of Seniors at Risk in identifying elderly people at risk of adverse outcomes (return to the emergency department, hospital admission, and death) within 30 days following discharge from the emergency department. Materials and Methods: 396 patients aged between 65 and 98 (mean 76.89±7.59) accessing the emergency department were evaluated over a 1-month period. Both screening tool were administered in the emergency department by emergency specialist physicians. Risk factors were assigned a score based on their regression co-efficient estimate and a total risk score was created. This score was evaluated for sensitivity and specificity. Results: Of the 396 participants, 198 (50%) were female. A significant correlation was not observed between risk of adverse outcomes and characteristics of the participant (p&gt;0.005). The Identification of Seniors at Risk (cutoff of ≥3) was positive in 61.3% of the patients, whereas 79% were Triage Risk Stratification Tool-positive (cutoff of ≥2). The two scores were significantly correlated and had similar areas under the receiver operating characteristic curves in predicting hospital admission (Identification of Seniors at Risk, 0.63; Triage Risk Stratification Tool, 0.59). Conclusions: The predictive accuracy of the scoring systems for hospital admission after 30 days was significant at cutoff values of ≥3 for Identification of Seniors at Risk and ≥2 for Triage Risk Stratification Tool. The Identification of Seniors at Risk had slightly higher sensitivity and lower specificity than the Triage Risk Stratification Tool. Key Words: Emergency Service, Hospital; Geriatric Assessment; Risk Assessment; Aged.

The Growth Effect of Government Expenditure in Jammu &amp; Kashmir

Purpose : The paper examined the growth impact of aggregate government expenditure and its various components in Jammu &amp; Kashmir using time series data from 1981–2020. At the disintegrated level, the study assessed the growth impact of government expenditure on three different types of services, namely, economic, social, and general. Methodology : The autoregressive distributed lag (ARDL) or bounds cointegration technique was used to explore the long-run relationship (cointegration) between total government expenditure and economic growth. The growth impact of government expenditure on economic, social, and general services was investigated using the regression model. Findings : The study found a cointegrating relationship between aggregate government expenditure and economic growth in the erstwhile state of Jammu &amp; Kashmir. The ARDL estimates showed that total government expenditure impacted Jammu &amp; Kashmir’s economic growth positively and significantly in the short and long run. Furthermore, the estimated regression coefficients revealed that government expenditure on social services impacted economic growth positively and significantly. In contrast, government expenditure on economic and general services had a negative but insignificant growth impact. Practical Implications : The study’s findings would assist the policymakers in prioritizing Jammu &amp; Kashmir’s limited resources for developing sectors with the potential to raise economic growth. The study recommended that government expenditure on social services should be increased to enhance growth in the erstwhile state of Jammu &amp; Kashmir. A compositional change in government expenditure favoring social services from general and economic services would stimulate economic growth in the former state of Jammu &amp; Kashmir. The study’s main limitation is that the other potential growth determinants were not considered explanatory/control variables. Originality : The study provided empirical evidence of the growth effect of total government expenditure and its various types in one of India’s least developed, slow-growing, and resource-deficit states (now Union Territory). To the best of my knowledge, the growth impact of government expenditure on three different types of services (economic, social, and general) were not investigated empirically in Jammu &amp; Kashmir for the period from 1981–2020. The study contributed to the scant empirical literature on the growth impact of government expenditure at the sub-national (state level).

Regularized fingerprinting in detection and attribution of climate change with weight matrix optimizing the efficiency in scaling factor estimation

Detection and attribution analyses play a central role in establishing the causal effect of human activities on global warming. The most commonly used method in such analyses, optimal fingerprinting, is a multiple regression where each covariate has a measurement error whose covariance matrix is the same as that of the regression error up to a known scale. Inferences about the regression coefficients are critical not only for making statements about detection and attribution but also for quantifying the uncertainty in important outcomes derived from detection and attribution analyses. When there are no errors-in-variables (EIV), the optimal weight matrix in estimating the regression coefficients is the precision matrix of the regression error. This matrix, however, is never known and has to be estimated from climate model simulations with regularization. The consequence is that the optimal fingerprinting method is not optimal, as believed in practice. We construct a weight matrix by inverting a nonlinear shrinkage estimate of the error covariance matrix that minimizes loss functions directly targeting the uncertainty of the resulting regression coefficient estimator. The resulting estimator of the regression coefficients is asymptotically optimal as the sample size of the climate model simulations and the matrix dimension go to infinity together with a limiting ratio. When EIVs are present, the estimator of the regression coefficients, based on the proposed weight matrix, is asymptotically more efficient than that based on the inverse of the existing linear shrinkage estimator of the error covariance matrix. The performance of the method is confirmed in finite sample simulation studies mimicking realistic situations in terms of the length of the confidence intervals and empirical coverage rates for the regression coefficients. In an application to detection and attribution analyses of the mean temperature at different spatial scales, the method yielded shorter confidence intervals which are important for such analyses in practice.

QR분해를 사용한 RHadoop의 맵리듀스 기반 다중선형회귀모형 추정 알고리즘

RHadoop enables R users to perform big data analytics under R programming environment by integrating R with Hadoop that supports distributed storing and parallel processing of large-scaled data. This article proposes a RHadoop-based MapReduce programming model for estimating multiple linear regression models utilizing QR factorization. Our proposed algorithm accommodates the most common type of big data that has a vast number of data points with only a few hundred variables. For QR factorization over massive-scaled data, our algorithm employs DirectQR method proposed by Benson, Gleich, Demmel(2013); however, it does not necessitate its iterative steps to estimate regression coefficients. Through a comparative simulation study, our algorithm is compared with a MapReduce-based algorithm proposed in the previous studies. For generating realistic synthetic data, we utilize NYC(New York City) yellow taxi trip data reported to NYC Taxi and Limousine Commission. In the simulation, we measure estimation time and accuracy of each algorithm under several assumptions with respect to the strength of association between independent variables and the noise level of error terms in the regression models.

Kernel Estimation of Bivariate Time-Varying Coefficient Model for Longitudinal Data with Terminal Event

We propose a nonparametric bivariate time-varying coefficient model for longitudinal measurements with the occurrence of a terminal event that is subject to right censoring. The time-varying coefficients capture the longitudinal trajectories of covariate effects along with both the followup time and the residual lifetime. The proposed model extends the parametric conditional approach given terminal event time in recent literature, and thus avoids potential model misspecification. We consider a kernel smoothing method for estimating regression coefficients in our model and use cross-validation for bandwidth selection, applying undersmoothing in the final analysis to eliminate the asymptotic bias of the kernel estimator. We show that the kernel estimates follow a finite-dimensional normal distribution asymptotically under mild regularity conditions, and provide an easily computed sandwich covariance matrix estimator. We conduct extensive simulations that show desirable performance of the proposed approach, and apply the method to analyzing the medical cost data for patients with end-stage renal disease. Supplementary materials for this article are available online.