Estimation Of Linear Models Research Articles

Nascent entrepreneurs’ progress in the venturing process: the role of proactiveness and cultural values

Understanding what entrepreneurs do and how their actions relate to successful new venture creation is a central research topic in the field of entrepreneurship. However, a comprehensive understanding of what leads to venture success is currently lacking. In our study, we explore the role of nascent entrepreneurs’ proactiveness in the process of new venture creation in different national cultural settings. We use a generalized linear model estimation with a negative binomial probability distribution to analyze a sample of 22,163 nascent entrepreneurs from 32 countries to assess the effects of proactiveness and national culture on venturing progress. Our findings demonstrate that individual proactiveness positively relates to the progress of new venture creation by nascent entrepreneurs. The dominance of embeddedness and harmony values is associated with lower levels of progress in the venturing process, although in more autonomous and mastery cultures the effect of proactiveness on venturing process is stronger. Our research highlights the importance of both individual-level characteristics and country-level contextual variables when studying nascent entrepreneurship.

Forensic discrimination between traditional and compressive imaging by blurring kernel investigation

AbstractImage forensics encompasses a set of scientific tests to investigation of a suspected event via intrinsic clues of imaging pipeline. Traditional image sensing at the Nyquist-Shannon rate as well as the new modality of compressive imaging below the rate are two main types of sensing in photography and imaging applications. Hence, for forensic investigators, it would importantly necessitate the ability to discriminate among images captured by them. However, due to the complex nonlinear nature of imaging processes, investigating imagers’ traces is a difficult task. To this intent, we first systematically model the imaging pipelines as an encoder-decoder pair. For exploring distinguishable traces, we mathematically simplify and linearize the pair for compressive imaging and two main forms of traditional image sensing with or without compression. Our theoretical analyses on the approximate linear models reveal blurring kernels of different imagers have discriminability. To validate it in real-world scenarios, we considered the whole imaging process as an inverse problem and estimated the blurring kernel based on a deconvolution approach, where the discriminability is also justified by information visualization. Then, we designed a pipeline classification system, where a deep convolutional neural network is trained by the estimated blurring kernels to be able to classify the three imaging systems. Our results in compressive imaging identification show an accuracy improvement about 3.7 % in comparison to the best result among compared methods. Implementation codes are available for research and development.

A Novel Minimum Distance Constraint Method Enhanced Dual-Foot-Mounted Inertial Navigation System for Pedestrian Positioning

Foot-mounted inertial navigation system (Foot-INS) with the zero velocity update (ZUPT) has become one of the indispensable technical means in professional pedestrian positioning fields due to the advantages of self-constraint and immune to environmental factors. The dual-Foot-INS can provide more excellent autonomous positioning performance than a single-Foot-INS because it utilizes more opportunities for zero velocity correction and additional distance constraint information. However, the classical dual-Foot-INS does not fully exploit the distance constraint potential for positioning improvement. In this paper, we proposed a novel minimum distance constraint (MDC) method that achieves higher positioning accuracy than the traditional dual-Foot-INS methods. To obtain an accurate and consistent state estimation under the nonlinear distance constraint problem, we propose an iterative distance constraint (IDC) algorithm. The IDC is transformed into an approximate linear constraint model, and an alternative estimate is obtained by the estimation projection method. To solve the problem that the distance constraint moment in the traditional method is affected by the recursive foot positions, we propose a more reasonable and reliable minimum distance moment detection (MDMD) method. The proposed MDMD method maximizes the positioning performance improvement of the dual-foot pedestrian system. Two rigorous experimental tests with a long walking trajectory without turn-around and close-loop were conducted to verify the effectiveness of the proposed method, the positioning error of the proposed method is reduced by 83.5% and 62.9% compared to the classical ZUPT and minimum distance constraint methods, respectively.

Robust adaptive variable selection in ultra-high dimensional linear regression models

We consider the problem of simultaneous variable selection and parameter estimation in an ultra-high dimensional linear regression model. The adaptive penalty functions are used in this regard to achieve the oracle variable selection property with simpler assumptions and lesser computational burden. Noting the non-robust nature of the usual adaptive procedures (e.g. adaptive LASSO) based on the squared error loss function against data contamination, quite frequent with modern large-scale data sets (e.g. noisy gene expression data, spectra and spectral data), in this paper, we present a new adaptive regularization procedure using a robust loss function based on the density power divergence (DPD) measure under a general class of error distributions. We theoretically prove that the proposed adaptive DPD-LASSO estimator of the regression coefficients is highly robust, consistent, asymptotically normal and leads to robust oracle-consistent variable selection under easily verifiable assumptions. Numerical illustrations are provided for the mostly used normal and heavy-tailed error densities. Finally, the proposal is applied to analyse an interesting spectral dataset, in the field of chemometrics, regarding the electron-probe X-ray microanalysis (EPXMA) of archaeological glass vessels from the 16th and 17th centuries.

Resilience‐oriented expansion planning of multi‐carrier microgrid utilizing bi‐level technique

AbstractThis paper presents the generation and transmission expansion planning (GTEP) in electricity and gas networks by considering their resilience against floods and earthquakes. These networks supply electricity, heat, and gas consumption energies as a multi‐carrier microgrid. The scheme is expressed in the form of bi‐level optimization, the upper level of which is the minimization of generation and transmission planning cost (total investment cost and expected operating cost) in the mentioned networks constrained to the investment budget and the planning model of the mentioned elements. Lower‐level formulation minimizes the total expected annual operating cost of these networks and the expected outage cost of electricity, heat, and gas consumers in the event of floods and earthquakes. This formulation is bound by the power flow equations of electricity and gas networks, the operation and resilience constraints of the networks, and the limitation on generation capability. In this problem, the expected energy not‐supplied and the outage cost during natural disasters are considered resilience indicators. Next, a single‐level model for the proposed design is extracted from the Karush–Kuhn–Tucker (KKT) method. The basic requirement of this method is the convexity of the lower‐level constraints. For this purpose, first, a linear approximation model is obtained for the lower‐level constraints of the problem. Furthermore, stochastic optimization is adopted to model the uncertainty of load, renewable power, and network equipment availability during floods and earthquakes. Finally, the extracted numerical results confirm the capability of the proposed scheme in improving the operation and resilience of the mentioned networks using optimal generation and transmission planning.

Genetic-parameter estimation of milk yield in White Maritza sheep breed using different test day models.

The aims of this study were to estimate the genetic parameters of the test day milk yield (TDMY) of the White Maritza sheep breed population and to choose the most appropriate linear models for genetic-parameter estimation of test day milk yield. The White Maritza sheep breed is a multipurpose native sheep breed in Bulgaria. Test day milk yield data were collected from 1992 to 2015 (24years). Milk yield recordings were made in 18 flocks according to the AC method (official milk recording by ICAR regulations). The database includes 8768 test day milk yield records belonging to 987 ewes. The pedigree file includes 1937 animals. Nine test day models (TDMs) were formulated and tested for the estimation of the genetic parameters of milk yield. The first three models were repeatability models (REP models), the second three were random regression models (RRMs), and the last three models were also random regression models with an added Ali and Schaeffer regression to describe the lactation curve using first-, second- and third-order polynomials. The average TDMY was 764.47 mL. There were no significant differences in the values of heritability () calculated by the three REP models: REP1 0.355 0.060, REP2 0.344 0.047 and REP3 0.347 0.060. The same applied to the repeatability coefficients, which, for the three REP models, were 0.384 0.065, 0.376 0.051 and 0.378 0.065, respectively. Based on REP model 1, three models with random regression RRM1, RRM2 and RRM3 were constructed, which is associated with the use of first-, second- and third-order polynomials (for the random effects of both the animal and the permanent environment). The trajectories of calculated by the three RRMs were not similar and demonstrated some differences, both at the beginning and in the middle of the milking period. The RRM with third-order polynomials demonstrated more genetic diversity until the 165th day of lactation, but Akaike information criterion (AIC), Bayesian information criterion (BIC) and log-likelihood (LogL) estimates were higher. The regression models with first- and second-degree polynomials were insufficient to reveal genetic diversity to a higher degree than REP model 1. The trend in the trajectories of calculated by the three random regression models with Ali and Schaeffer regression models (ASRMs) was similar to that of random regression models without the Ali and Schaeffer regression incorporated. Although the noted advantages of the random regression models revealed, to a greater extent, the genetic diversity of test day milk yield, AIC, BIC and LogL estimates indicated that repeatability models achieved a better balance between complexity and fitness and a smaller prediction error compared to random regression models.

The Consistency of LSE Estimators in Partial Linear Regression Models under Mixing Random Errors

The Consistency of LSE Estimators in Partial Linear Regression Models under Mixing Random Errors

Simple approaches to nonlinear difference-in-differences with panel data

Summary I derive simple, flexible strategies for difference-in-differences settings where the nature of the response variable may warrant a nonlinear model. I allow for general staggered interventions, with and without covariates. Under an index version of parallel trends, I show that average treatment effects on the treated (ATTs) are identified for each cohort and calendar time period in which a cohort was subjected to the intervention. The pooled quasi-maximum likelihood estimators in the linear exponential family extend pooled ordinary least squares estimation of linear models. By using the conditional mean associated with the canonical link function, imputation and pooling across the entire sample produce identical estimates. Generally, pooled estimation results in very simple computation of the ATTs and their standard errors. The leading cases are a logit functional form for binary and fractional outcomes—combined with the Bernoulli quasi-log likelihood (QLL)—and an exponential mean combined with the Poisson QLL.

FACTORS THAT AFFECT FIRM VALUE IN MANUFACTURING FIRMS

This research aims to obtain empirical evidence from the effect of profitability, leverage, liquidity, firm size, and activity ratio on firm value. Totals of 198 observational data originated from 66 manufacturing companies listed on Indonesia Stock Exchange (IDX) throughout 2018-2020. The sampling technique used in this research is purposive sampling. The hypothesis testing method in this research uses the multiple linear regression model. The multiple linear regression model estimation that suits this research is Fixed Effect Model (FEM). The statistical tool used for data processing in this research is EViews 12. This research uses Tobin's Q as a parameter to measure firm value. Based on the outcome of the processed data, profitability, leverage, and firm size all have significant effects on firm value, while liquidity and activity ratio both have insignificant effect on firm value.

FACTORS THAT AFFECT FIRM VALUE IN MANUFACTURING FIRMS

This research aims to obtain empirical evidence from the effect of profitability, leverage, liquidity, firm size, and activity ratio on firm value. Totals of 198 observational data originated from 66 manufacturing companies listed on Indonesia Stock Exchange (IDX) throughout 2018-2020. The sampling technique used in this research is purposive sampling. The hypothesis testing method in this research uses the multiple linear regression model. The multiple linear regression model estimation that suits this research is Fixed Effect Model (FEM). The statistical tool used for data processing in this research is EViews 12. This research uses Tobin's Q as a parameter to measure firm value. Based on the outcome of the processed data, profitability, leverage, and firm size all have significant effects on firm value, while liquidity and activity ratio both have insignificant effect on firm value.

Estimating canopy parameters of winter wheat at different stages using hyperspectral data combined with soil variables

ABSTRACT Remote sensing technology has many advantages in real-time estimating of crop growth. Crop growing estimation primarily establishes the direct statistical model between spectral data and crop biophysical variables. However, the model accuracy rarely improves with the increase in the number of independent variables owing to the multicollinearity; hence, the addition of a data set different from spectral variables may solve this problem. Considering the strong correlation between crop growth and soil environment, this study aims to investigate whether and how the addition of important soil variables can improve estimation model accuracy. This study collected LAI and Chlorophyll substitution index (SPAD value) of wheat canopy and canopy spectral data under different soil environments to quantify the correspondent relationship. Important spectral parameters (IPs) and important soil variables (ISVs) were selected by least absolute shrinkage and selection operator (LASSO) to establish linear and nonlinear models for wheat growth estimation and the effect of multiple soil variables in enhancing wheat growth estimation was tested. The results indicated LASSO can effectively reduce feature dimensionality for wheat growth estimation with maintaining model accuracy; the extra ISVs can improve the model accuracy due to the high collinearity of spectral parameters. The optimal models of wheat LAI estimation (R2 = 0.83, RMSE = 0.500) and SPAD estimation (R2 = 0.75, RMSE = 1.835) were constructed based on orthogonal partial least squares analysis (OPLS) by IPs and IPs+ISVs, respectively. Finally, we discussed the applicability of spectral parameters and soil variables. This research combines remote sensing features of crops with crucial growth variables to obtain an efficient and mechanical crop growth estimation.

The Influence of Participatory Technology and Institutional Participation of Farmer Groups on Farmer Business Development

Janti Village, Jogoroto, Jombang has a corn productivity of 2020 reaching 84.5 Kw/Ha, this achievement exceeds national average corn productivity. However, this is inversely proportional to the unstable price of corn, which can even be far from the normal price. Besides, the lack of farmer participation makes the problem drag on longer. The existence of farmer group institutions is very important to solve this problem. In addition, the application of participatory technology to farmers also provides clear goals for future agricultural production activities. Therefore, it is necessary to be active and participate in the Janti Village farmer group members. The purpose of this study is to describe the influence of participatory technology (discussions, workshops, and action planning) and institutional participation of farmer groups (participation in planning, implementation, utilization of results, and evaluation) on farming development (social, economic, and environmental) in Janti Village. Jogoroto District, Jombang Regency. The research method uses descriptive quantitative data collection techniques namely interviews, observation, and questionnaires. Previously, the questionnaire had to be tested for validity and reliability tests to support the validity of the research data obtained. The sampling method used is a simple random sample. Then the data analysis used is multiple linear regression analysis using the ordinary least square method including data tabulation, estimation of multiple linear regression models, classical assumption tests, regression feasibility tests, and interpretation of multiple linear regression models. Results showed that there was no significant effect if independent variables moved independently on dependent variables. Meanwhile, if independent variables move simultaneously, there will be a significant influence between participatory technology and the institutional participation of farmer groups in the development of farming businesses.

Adaptive dynamic programming for data-based optimal state regulation with experience replay

Traditional model-based control methods require accurate system dynamics. However, the dynamics are usually unknown and it is challenging to tune the control parameters manually when controlling a complex nonlinear system. In this paper, we propose a novel reinforcement learning method that combines the advantages of a model-based method, namely Adaptive Dynamic Programming (ADP), with the actor-critic method. Specifically, a linear approximate model is chosen to obtain the estimated dynamics as part of the optimal policy. Then, a designed actor-critic structure is used to obtain the sub–policy. We provide the theoretical proof of convergence and validate the proposed method through simulation experiments. The experimental results demonstrate the effectiveness of the proposed method with smaller tracking errors and faster learning speed compared with the controller trained by the actor-critic method.

Adverse Events Related to SARS-Cov-2 Vaccination: A Systematic Review and Meta-Analysis

Background: Vaccination has been adopted as a key public health strategy for combating the COVID-19 pandemic. The accelerated SARS-CoV-2 vaccines’ development had limited time for extensive investigation of the adverse events. The study aimed to assess the average adverse events rates in published COVID-19 vaccination studies. Subjects and Method: The study used systematic review and meta-analysis involving studies that reported adverse events following administration of any of the approved COVID-19 vaccines in humans. A highly specific search strategy was developed and implemented in PubMed. The core search string was “(COVID-19 OR COVID OR "coronavirus disease") AND vaccin* AND (side-effects OR "adverse events")”. Titles and abstracts were screened, and full texts of potentially relevant articles were retrieved. Data extracted included general study background, adverse events, and frequency of occurrence. Meta-analyses were conducted for adverse events reported by at least 5 studies. Meta-analysis of proportions was carried out using logit transformation with the generalized linear mixed model estimation method. Results: A total of 108 adverse events were reported in 15 studies observing 735,515 participants from 10 countries. The highest pooled prevalence rates were pain at injection site (67.2%; 95% CI= 46.49 to 82.86; I2= 99.9%, 11 studies, 670,557 participants), weakness/fatigue (41.88%; 95% CI= 26.82 to 58.61, I2= 99.9%, 13 studies, 671,045 participants), muscle/joint pain (28.95%; 95% CI= 16.95 to 44.86, I2= 99.9%, 13 studies, 672,791 participants), and headache (27.78%; 95% CI= 17.59 to 40.95, I2= 99.9%, 14 studies, 672,883 participants). Four cases of death were reported by two papers enrolling 711 patients with cancer or multiple sclerosis, three due to comorbid disease progression, and one case due to COVID-19. Forty-three cases of anaphylaxis were reported in three studies enrolling 68,218 participants. Conclusion: The most prevalent adverse events among recipient of SARS-CoV-2 vaccines were local and general systemic reactions. Keywords: COVID-19, SARS-CoV-2 vaccine, adverse events, meta-analysis, systematic review Correspondence: Segun Bello. Department of Epidemiology and Medical Statistics, Faculty of Public Health, College of Medicine, University of Ibadan, Nigeria. Email: drsegunbello@yahoo.com.

The DeCAMFounder: nonlinear causal discovery in the presence of hidden variables

Abstract Many real-world decision-making tasks require learning causal relationships between a set of variables. Traditional causal discovery methods, however, require that all variables are observed, which is often not feasible in practical scenarios. Without additional assumptions about the unobserved variables, it is not possible to recover any causal relationships from observational data. Fortunately, in many applied settings, additional structure among the confounders can be expected. In particular, pervasive confounding is commonly encountered and has been utilised for consistent causal estimation in linear causal models. In this article, we present a provably consistent method to estimate causal relationships in the nonlinear, pervasive confounding setting. The core of our procedure relies on the ability to estimate the confounding variation through a simple spectral decomposition of the observed data matrix. We derive a DAG score function based on this insight, prove its consistency in recovering a correct ordering of the DAG, and empirically compare it to previous approaches. We demonstrate improved performance on both simulated and real datasets by explicitly accounting for both confounders and nonlinear effects.

BIM+ Engineering Cost Estimation Optimization Based on Equivalent Approximate Linear Model

Abstract In this paper, a typical equivalent fitting linear modeling method is studied by using BIM technology. This paper realizes the quality-efficiency control of construction projects based on BIM technology. The software can realize the cost data statistics and collection of each sample. The system takes the statistical sequence of each department’s project cost as the original data for dimensionality reduction processing. The variance contribution is used as the weight to calculate the weight of each main component. According to the finite element analysis of combined parameters, the project cost and construction benefit are the restrictive factors. A multi-factor comprehensive evaluation method establishes a rapid construction cost evaluation model. The experimental results show that this method agrees with the conventional calculation method and the measured data. The estimated cost of construction differs little from the measured value. The results show that the model constructed according to different stages of the construction projects has good prediction accuracy.

Asymptotic properties of M estimators in classical linear models with φ-mixing random errors

In this paper, the asymptotic normality and strong consistency for M estimators of the unknown regression coefficients in classical linear models are established under the assumptions that the errors are identically distributed φ-mixing random variables. The results obtained in this paper generalize the corresponding ones of Chen and Zhao [M-methods in linear model. Shanghai: Scientific and Technical Publishers; 1996] and Zhao [Strong consistency of M-estimates in linear model. Sci China Ser A. 2002;45:1420–1427] for independent errors. Finally, the simulation study is provided to verify the finite sample performance of the theoretical results and a real example is analysed for illustration.

Gender Responsive: Inequality Development in Islands Bali, Indonesia

Aims: Sustainable development goals do not focus on the economy alone, but also on gender equality. Since Indonesia implemented regional autonomy (1999), it is hoped that the development goals will be realized. However, the facts show that income and gender inequality still exists. The Province of Bali is also faced with this problem. Therefore, this study aims to determine trends and analyze the effect of development inequality as measured by Gini Ratio, Gross Regional Domestic Product at constant prices (GRDP-cp), on gender inequality as measured by the human development index (HDI) and development index gender (IPG).
 Study Design: This Study used a quantitative descriptive method.
 Place and Duration of Study: Regional Province of Bali as the location of the study. The Province of Bali is one of 33 Provinces in Indonesia. The Province of Bali consists of small islands namely Bali Island, Nusa Penida Island and several others having an area of 5,632.82 square kilometers. Administratively, Bali Island consists of eight regencies, one city area.
 Methodology: This study aims to determine the trend of the gini ratio, GRDP-cp, human development index (HDI) and gender development index (GDI) and also analyze the relationships that influence each of these variables. The approach used in this research is quantitative with secondary data in the form of panel data obtained from the Indonesian Central Bureau of Statistics (BPS) for period (2010 - 2017) are used in the analysis. This data is sourced from the annual reports of the Central Statistics Agency (BPS) of the regencies / cities and the Province of Bali. Several statistical models are applied in accordance with the objectives of the study, consisting of graphs, linear and nonlinear trend models and multiple linear regression estimation models. The method used to analyze gender responses: development inequality in Bali is panel data regression.
 Results: Gini ratio (GR) has a positive and significant effect at a significance level of 5 percent, towards the gender development index (GDI), GRDP-cp has a positive and significant effect at the 5 percent significance level, towards the growth of the gender development index (GDI), The human development index (HDI) has a positive and significant effect at the 5 percent significance level, on the gender development index (GDI).
 Conclusion: Human development that leads to gender equality at the Regency/Municipality level in the Province of Bali continues to progress until the end of 2017, and if the conditions of the regional economy are able to be maximally improved then the success of sustainable human development is not impossible to achieve and, However, until now the facts show that the results of development achieved at the Regency/Municipality level in Bali Province still have income and gender inequalities, although it has been attempted to be minimized by various policies carried out by the government. Some areas of development in this area that still need to be completed are income and gender inequalities in the fields of economics, and education.

Advances in estimation and inference for historical functional linear models

AbstractHistorical functional linear models or HFLMs are a class of function‐on‐function regression models that seek to restrict the relationship between two or more time‐dependent functions, or curves, of data where one function is a natural outcome and the others are natural predictors. A natural predictor is a predictor that occurs in time before, or at most concurrently with, an outcome. The primary challenge in developing methods for HFLMs is ensuring that the time‐dependent relationship between the outcome and predictors is enforced and that no “unnatural” relationships are allowed, for example, where estimation, inference, or prediction are conducted on or using coefficients such that the outcome occurs before the predictor. A number of authors consider a variety of modeling frameworks for HFLMs. This work seeks to introduce the basic HFLM, explore the various approaches for its estimation, and discuss recent advances.This article is categorized under: Statistical Models > Linear Models Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data Statistical Models > Model Selection

447-P: Using the Cambridge Risk Score to Predict New Hyperglycemia and Complications in Surgical Patients without Diabetes

Perioperative hyperglycemia is associated with adverse surgical outcomes and increased mortality, and when occurring in patients without history of diabetes, this is termed new hyperglycemia (NH). The Cambridge Risk Score (CRS) is a screening tool for type 2 diabetes that is associated with NH, yet its application in a surgical population has not been studied. Our study examined the association between CRS, NH, and complications in patients undergoing elective surgery. In this retrospective cross-sectional study of adult surgical patients admitted to a tertiary care center over 4 years, subjects with NH (blood glucose ≥140 mg/dL in nondiabetics) were identified, and the CRS was calculated. CRS, NH, and clinical outcomes were adjusted for race/ethnicity, payor type, and Elixhauser Comorbidity Index. Patients were stratified by surgery type (cardiac/vascular, general, orthopedic, and neurologic), and multivariate regression analyses assessed the association between CRS, NH, and clinical outcomes. Of 10,531 subjects included in the study, 24% developed NH. After adjusting for covariates, the CRS was significantly associated with the development of complications (odds ratio [OR], 2.09 [1.69, 2.59]; p<.001) and NH (OR, 1.95 [1.66, 2.29]; p<.001). When stratifying by surgery type, CRS was significantly associated with NH, length of stay, and complications in the neurosurgery and orthopedic surgery cohorts, but not cardiac/vascular or general surgery. NH, when adjusted for covariates, was associated with 30-day readmission rates (OR, 1.52 [1.23, 1.86]; p<.001) and length of stay (linear model estimate, 0.61 [0.57, 0.66]; p<.001), but not with complication outcomes. CRS was associated with the development of NH and complications in elective neurological and orthopedic surgery patients and could help identify high-risk surgical patients. Further study should evaluate if treatment of NH is associated with better outcomes in CRS high-risk patients. Disclosure H.Lee: None. P.J.Hartfield: None. G.Sinson: None. M.C.Wang: Consultant; Medtronic, Zimmer Biomet. A.Z.Dawson: None. A.Thorgerson: None. C.E.Mendez: Speaker's Bureau; Siemens.