Estimation Of Linear Models Research Articles

COVID-19 in Pakistan: A national analysis of five pandemic waves.

The COVID-19 pandemic showed distinct waves where cases ebbed and flowed. While each country had slight, nuanced differences, lessons from each wave with country-specific details provides important lessons for prevention, understanding medical outcomes and the role of vaccines. This paper compares key characteristics from the five different COVID-19 waves in Pakistan. Data was sourced from daily national situation reports (Sitreps) prepared by the National Emergency Operations Centre (NEOC) in Islamabad. We use specific criteria to define COVID-19 waves. The start of each COVID-19 wave is marked by the day of the lowest number of daily cases preceding a sustained increase, while the end is the day with the lowest number of cases following a 7-days decline, which should be lower than the 7 days following it. Key variables such as COVID-19 tests, cases, and deaths with their rates of change to the peak and then to the trough are used to draw descriptive comparisons. Additionally, a linear regression model estimates daily new COVID-19 deaths in Pakistan. Pakistan saw five distinct waves, each of which displayed the typical topology of a complete infectious disease epidemic. The time from wave-start to peak became progressively shorter, and from wave-peak to trough, progressively longer. Each wave appears to also be getting shorter, except for wave 4, which lasted longer than wave 3. A one percent increase in vaccinations decreased deaths by 0.38% (95% CI: -0.67, -0.08) in wave 5 and the association is statistically significant. Each wave displayed distinct characteristics that must be interpreted in the context of the level of response and the variant driving the epidemic. Key indicators suggest that COVID-19 preventive measures kept pace with the disease. Waves 1 and 2 were mainly about prevention and learning how to clinically manage patients. Vaccination started late during wave 3 and its impact on hospitalizations and deaths became visible in wave 5. The impact of highly virulent strains Alpha/B.1.1.7 and Delta/B.1.617.2 variants during wave 3 and milder but more infectious Omicron/B.1.1.529 during wave 5 are apparent.

Determinants of Asymmetric Costs In Soya Production

Purpose: The study aimed to analyze the determinants of the asymmetric behavior of fixed costs in soybean production. Theoretical background: Although previous research indicates the presence of asymmetric costs (sticky costs theory) in the agricultural sector and, specifically, in soybean production, there is still a gap in testing which determinants explain this asymmetric condition. Methodology: The study used a quantitative methodology, with the estimation of a multiple linear regression model and parametric correlation tests, using data on costs, production capacity, production quantity and soybean prices for the period from 2009 to 2022. Results and conclusion: The results showed that the capacity and price factors were statistically significant in explaining the asymmetry of fixed costs. Price was also significant in explaining total and variable costs, which were also shown to be asymmetrical by the models used. Research implications: The study advances in verifying the determining factors of asymmetry and not just in pointing out the behavior of costs in this sector, as previous literature has done. Originality/value: The approach of the work and its results contribute to expanding empirical research related to the assumptions of the sticky costs theory in a relevant economic activity.

A data-driven Vertical Stabilization system for the ITER tokamak based on Dynamic Mode Decomposition

In this article, we propose an online method to estimate an approximate linear model of a vertically unstable tokamak plasma to consequently adapt the parameters of a Vertical Stabilization controller. The identification procedure is based on the Dynamic Mode Decomposition with control approach, while the tuning procedure takes advantage of linear control theory to impose the desired crossing frequency and gain margins. The proposed technique is aimed at controlling ITER elongated plasmas using the VS3 stabilization coils, located inside the vessel. The effectiveness of the method is proven by means of numerical simulations carried out with the CREATE-NL+ free boundary evolutionary code, also considering the presence of realistic measurement noise levels.

Association analysis of self-reported outcomes with a validated subset.

In health-science research, outcomes ascertained through surveys and interviews are subject to potential bias with respect to the true outcome status, which is only ascertainable with clinical and laboratory assessment. This measurement error may lead to biased inference when evaluating associations between exposures and outcomes of interest. Here, we consider a cohort study in which the outcome of interest is ascertained via questionnaire, subject to imperfect ascertainment, but where a subset of participants also have a clinically assessed, validated outcome available. This presents a methodological opportunity to address potential bias. Specifically, we constructed the likelihood in two parts, one using the validated subset and the other using a subset without validation. This work expands on that proposed by Pepe and enables inference with standard statistical software. Weighted generalized linear model estimates for our method and maximum likelihood estimates (MLE) for Pepe's method were computed, and the statistical inference was based on the standard large-sample likelihood theory. We compare the finite sample performance of two approaches through Monte Carlo simulations. This methodological work was motivated by a large cohort study of long-term childhood cancer survivors, allowing us to provide a relevant application example where we examined the association between clinical factors and chronic health conditions.

Huber-Dutter estimation of linear models with dependent errors

In this article, we investigate a linear model that incorporates stationary causal processes, with a focus on utilizing Huber-Dutter methods to investigate the estimators of unknown parameters and a scale for the errors. Our results indicate that the Huber-Dutter methods can effectively be utilized for linear models that feature errors, which are short-range dependent linear processes, heavy-tailed linear processes, as well as some commonly used non linear time series.

Revising the oldest Oldowan: Updated optimal linear estimation models and the impact of Nyayanga (Kenya)

Revising the oldest Oldowan: Updated optimal linear estimation models and the impact of Nyayanga (Kenya)

Zastosowanie programowania nieliniowego dla konfiguracji systemu wymiany ciepła w aparatach kolumnowych przemysłu petrochemicznego przy minimalnych kosztach

For increasing the efficiency of heat transfer processes, the layer-by-layer concept of arrangement of various types of packings in column apparatuses is considered. The results of hydraulic tests of a dry and irrigated cell packing, as well as individual hydraulic tests of a mesh packing, are presented. Methods and algorithms for solving linear programming problems under “uncertainty” conditions were considered. However, in some areas of science it is often difficult or impossible to formalize the problem in an appropriate way and reduce it to a linear programming problem. In this paper, methods for solving non-linear programming problems with a vector objective function are considered. At present, the use of non-linear programming in the vast majority of real situations is reduced to linear approximation models. Along with this, at a significant non-linearity, due to its specificity or influence on the nature of the model, it is necessary to apply optimization methods that are much more complex than, for example, the simplex method. However, the importance of non-linear programming is constantly increasing. This is due to the rapidly growing knowledge of managers and specialists in the use of mathematical models designed to prepare solutions, as well as the increasing availability of computer programs for solving large-scale nonlinear problems. The analysis and studies of the hydrodynamics of a number of regular packings have shown that cell and mesh packings are promising for the implementation of the phase inversion mode. The proposed concept of intensifying heat transfer processes in column apparatuses is based on the use of layers of various packings arranged in the following order in the apparatus, type 1 – cell, type 2 – mesh. At the same time, their main geometric characteristics differ significantly from each other. However, the structure of the bulk packing is inhomogeneous, which makes it difficult to implement a stable mode of local phase inversion under these conditions. This is due to the structure of column apparatuses with bulk packing, in which there is an increased proportion of pores (porosity) near the walls of the apparatus. Porosity is very significant; it can reach up to 40%, and as a result, the local velocity near the walls exceeds the velocity in the centre of the apparatus by up to 70%. By contrast, the use of regular packing structures makes this potentially highly efficient mode technically possible.

Transcriptomic signatures of regional selective vulnerability in ALS and FTLD‐TDP

AbstractBackgroundDifferential gene expression in the brain has been identified in both amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration with TDP‐43 (FTLD‐TDP) relative to controls. However, direct comparisons of ALS and FTLD‐TDP are rare. We hypothesize that identification of distinct transcriptomic signatures between ALS and FTLD‐TDP may provide insight into regional selective vulnerability of TDP‐43. Here, we directly compare gene expression in frontal cortex between FTLD‐TDP and ALS cases, accounting for cell‐type proportion differences due to neurodegeneration.MethodFrontal cortex RNA‐seq samples passing extensive quality control metrics were downloaded from the New York Genome Center ALS Consortium, including non‐neurological controls (n = 42) and individuals with neuropathological diagnoses of ALS (n = 118) or FTLD‐TDP (n = 27). A total of 59,782 genes were quantified for each sample. Scaden was used to deconvolve this bulk RNA‐seq data into cell type proportions. Infrequently expressed genes were filtered and data was normalized by total count. Generalized linear model estimates of dispersion were calculated accounting for potential confounders including cell type proportions, RIN, contributing site, age at death, and sex to identify differentially expressed genes (DEGs) between ALS and FTLD‐TDP as well as each neurological group relative to controls. A gene ontology enrichment analysis for biological processes was performed for each DEG set. We report DEGs and enrichment surviving FDR p<0.05.Result653 DEGs were identified in FTLD‐TDP compared to ALS within the frontal cortex samples. The majority of these DEGs were also differentially expressed between only one of these neurological groups and controls. However, 281 of these DEGs were uniquely differentially expressed in ALS compared to FTLD‐TDP, not differentially expressed between either neurological group and controls. Gene ontology enrichment analyses for biological processes revealed that the 198 genes uniquely upregulated in FTLD‐TDP compared to ALS (not different from controls) mapped to pathway terms including those related to immune and inflammatory responses as well as vascular functions, while the 83 genes uniquely upregulated in ALS compared to FTLD‐TDP related to retina development.ConclusionThis case‐case comparative study will provide insight into the candidate mechanisms underlying regional selective vulnerability in FTLD‐TDP relative to ALS that may drive individual‐level cognitive/behavioral presentations of TDP‐43 proteinopathies.

A REML method for the evidence-splitting model in network meta-analysis.

Checking for possible inconsistency between direct and indirect evidence is an important task in network meta-analysis. Recently, an evidence-splitting (ES) model has been proposed, that allows separating direct and indirect evidence in a network and hence assessing inconsistency. A salient feature of this model is that the variance for heterogeneity appears in both the mean and the variance structure. Thus, full maximum likelihood (ML) has been proposed for estimating the parameters of this model. Maximum likelihood is known to yield biased variance component estimates in linear mixed models, and this problem is expected to also affect the ES model. The purpose of the present paper, therefore, is to propose a method based on residual (or restricted) maximum likelihood (REML). Our simulation shows that this new method is quite competitive to methods based on full ML in terms of bias and mean squared error. In addition, some limitations of the ES model are discussed. While this model splits direct and indirect evidence, it is not a plausible model for the cause of inconsistency.

The Pengaruh Inflasi, PDRB, IPM dan Upah Minimum terhadap Tingkat Pengangguran Terbuka di Pulau Sumatera

This study aims to determine the effect of inflation, Gross Regional Domestic Product (GDP), Human Development Index (IPM) and minimum wages on the Open Unemployment Rate on the island of Sumatra. This study uses secondary data in the form of panel data, with time series data from 2011 to 2020 and cross section data from 10 provinces on the island of Sumatra. Multiple linear model estimation analysis method with Fixed Effect Model (FEM) method. The results of the panel data estimation show that inflation has a negative and significant effect on the open unemployment rate, GDP has no effect on the open unemployment rate, HDI has a negative and significant effect on the open unemployment rate, and the minimum wage has a positive and insignificant effect on the open unemployment rate.

Buys-Ballot Estimates for Linear Model with the Expected Values in Time Series Decomposition

The study discusses the Buys-Ballot estimates for linear trend-cycle and seasonal indices with the expected values for mixed model in time series analysis. The emphasis is to derive the expected values of row, column and overall means of the of Buys-Ballot table for the mixed model. We use a real life data to determine the estimation of trend parameters, seasonal indices and choice of appropriate model of the Buys-Ballot table. Results indicate that, (1) the expected value of the row average mimic the shape of the trending parameters of the original series and contains seasonal effect in \[C_1=\sum_{j=1}^S j s_j\] (2) the expected value of the column average also mimic the shape of the trending curves of the original series and contain seasonal effect (3) the appropriate model that best describe the pattern of the study series listed in the summary table (Table 5) is mixed.

Distributed Inference Over Linear Models Using Alternating Gaussian Belief Propagation

We consider the problem of maximum likelihood estimation in linear models represented by factor graphs and solved via the Gaussian belief propagation algorithm. Motivated by massive internet of things (IoT) networks and edge computing, we set the above problem in a clustered scenario, where the factor graph is divided into clusters and assigned for processing in a distributed fashion across a number of edge computing nodes. For these scenarios, we show that an alternating Gaussian belief propagation (AGBP) algorithm that alternates between inter-and intra-cluster iterations, demonstrates superior performance in terms of convergence properties compared to the existing solutions in the literature. We present a comprehensive framework and introduce appropriate metrics to analyse AGBP algorithm across a wide range of linear models characterised by symmetric and non-symmetric, square, and rectangular matrices. We extend the analysis to the case of dynamic linear models by introducing dynamic arrival of new data over time. Using a combination of analytical and extensive numerical results, we show the efficiency and scalability of AGBP algorithm, making it a suitable solution for large-scale inference in massive IoT networks.

Do business ethics moderate corporate corruption risk-ESG reporting relationship? Evidence from European ESG firms

PurposeThis paper aims to explore the relationship between corporate corruption risk and environmental, social and governance (ESG) reporting and if this relationship is moderated by business ethics.Design/methodology/approachData from a sample of 347 European firms selected from the ESG Index between 2010 and 2020 were used to test the model using panel data and multiple regressions. This paper considered the feasible generalized least squares estimation for linear panel data models. A multiple regression model is used to analyze the moderating effect of business ethics on the association between corporate corruption risk and ESG reporting. For robustness analyses, the authors included the alternative measure of the dependent variable, and they applied the simultaneous equation model for the endogeneity test.FindingsThe empirical results reveal a negative relationship between corporate corruption risk and ESG reporting. Furthermore, the findings suggest that business ethics positively moderate the relationship between corporate corruption risk and ESG reporting.Practical implicationsThis paper presents an enormous contribution to the various economic agents involved in the company. The results could attract the attention of socially responsible investors and, above all, corporate citizens. Moreover, the managers of corrupt companies could take into account the results of this study by being more committed to an optimized transparency strategy on ESG reporting.Originality/valueTo the best of the authors’ knowledge, this is the first study to investigate the moderating role of business ethics on the relationship between corporate corruption risk and ESG reporting in the European context. It is also the first study documenting that business ethics reinforce the relationship between firm corruption and nonfinancial information transparency. This study fills a research gap as it expands the existing literature, which generally focuses on the impact of corporate corruption on ESG reporting.

A simulation-based approximate dynamic programming approach to dynamic and stochastic resource-constrained multi-project scheduling problem

We consider the dynamic and stochastic resource-constrained multi-project scheduling problem which allows for the random arrival of projects and stochastic task durations. Completing projects generates rewards, which are reduced by a tardiness cost in the case of late completion. Multiple types of resource are available, and projects consume different amounts of these resources when under processing. The problem is modelled as an infinite-horizon discrete-time Markov decision process and seeks to maximise the expected discounted long-run profit. We use an approximate dynamic programming algorithm (ADP) with a linear approximation model which can be used for online decision making. Our approximation model uses project elements that are easily accessible by a decision-maker, with the model coefficients obtained offline via a combination of Monte Carlo simulation and least squares estimation. Our numerical study shows that ADP often statistically significantly outperforms the optimal reactive baseline algorithm (ORBA). In experiments on smaller problems however, both typically perform suboptimally compared to the optimal scheduler obtained by stochastic dynamic programming. ADP has an advantage over ORBA and dynamic programming in that ADP can be applied to larger problems. We also show that ADP generally produces statistically significantly higher profits than common algorithms used in practice, such as a rule-based algorithm and a reactive genetic algorithm.

A linear model-based simulation tool for estimating number of trials needed for upper limb stroke recovery in a given rehabilitation session

Traditional methods for assessing upper-limb functional outcomes in stroke patients often fail to estimate the number of trials required to achieve performance stability of a chosen kinematic metric. Limited non-model-based studies have attempted to tackle this issue. To bridge this gap, this study utilized an iterative learning algorithm (ILA) in MATLAB, employing linear models to represent the muscle dynamics and forearm extension of impaired patients. The reference task space trajectory was set as a straight-line point-point trajectory within a range of 0 - 0.2828m. By using the root mean square error (RMSE) as a metric for evaluating kinematic accuracy, a maximum kinematic deviation error of 0.01m was imposed with respect to the trajectory by the (ILA). Results indicate that over 16 trials, performance stability was obtained with improvement in deviation error from 0.0168m in the first trial to 0.0060 at sixteen trials. The result obtained is in line with similar non-model studies and our findings inform the potential of ILAs with linear models for estimation of trial numbers required to attain performance stability of a selected kinematic metric (i.e., kinematic accuracy).

Indirect inference approach for parameter estimation of non linear manoeuvring models of a ROV based on model basin trials

ABSTRACT In this work, indirect inference (II) techniques are applied to estimate the parameters of a non-linear manoeuvring model of a remotely operated vehicle (ROV). In the application of this method, a set of auxiliary statistics is established in the optimisation process in order to improve the efficiency of the estimated parameters. For the parameter estimation, data from different tests are available, which were carried out with a ROV in the facilities of the Centro de Experiencias Hidrodinámicas del Pardo INTA/CEHIPAR, Madrid. The model obtained is validated by means of graphical and statistical methods with the acquired data and the statistical properties of the estimated parameters are evaluated by means of a Monte Carlo study.

Matrix spaces and ordinary least square estimators in linear models for random matrices

This article, using generalized inverses of matrices, studies the spaces whose elements are matrices. Based on the results obtained, for linear models for random matrices, the article explores the role of ordinary least square estimators in identifying linear estimable functions, and the role of minimum norm ordinary least square estimators in creating linear unbiased estimators. With added conditions on the covariance matrix for vectorized response, it is shown that a linear unbiased estimator constructed from the minimum norm ordinary least square estimator is a best linear unbiased estimator with respect to the risk induced from squared distance loss.

Flexible-reliable linear AC security constrained unit commitment considering uncertainties and renewable energy sources

In this paper, AC security-constrained unit commitment (AC-SCUC) is presented in the transmission network considering flexibility and reliability limits in the presence of renewable resources, energy storage and demand response plan. This minimization scheme considers the sum of expected operating costs and network reliability minus the benefit of network flexibility as the objective function. This problem is related to AC power flow equations, the operation model of storage, demand response, renewable sources, and non-renewable generation units, technical limits of the network, including indices of operation, reliability, flexibility, security. and the stability of the network. In this plan, load, renewable power, energy price, and availability of network equipment are uncertainties. In this paper, in order to accurately evaluate reliability and flexibility indices, stochastic planning is adopted to model the aforementioned uncertainties. The design has non-linear and non-convex constraints; therefore, to reach a unique solution in low computing time, the linear approximation model is incorporated. AC-SCUC linear modeling along with simultaneous modeling of operation, economic, flexibility, reliability, security, and stability indices along with stochastic modeling of uncertainties to accurately evaluate flexibility and reliability indices are considered as the contributions of this scheme. The obtained numerical results indicate the ability of the proposed scheme in improving the economic and technical condition of the transmission network with regard to the optimal management of energy sources, storage devices, and demand response. So, the scheme can obtain high reliability conditions for the transmission network by considering the high penalty price. Also, by considering a high incentive price for flexibility, non-renewable resources, storage devices, and demand response have been able to achieve high flexibility for the network. Optimal energy management of resources, storage devices, and demand response has significantly enhanced the network's operation status.

Linear mathematical models for yield estimation of baby corn (Zea mays L.)

Linear mathematical models have been developed for predicting baby corn yield in terms of cob volume for two cycles of maize (Zea mays L.). Cob volume is directly proportional to morphological parameters such as length, weight, and girth; hence, linear mathematical models have been developed. Primary data for a random selection of 60 cobs for each cycle were collected, and lab work was carried out to measure the corn ears and cob growth parameters. An irregular distribution was observed among all six growth parameters examined in the study. Descriptive statistical measures were employed to facilitate the description of growth parameters. The final volume of the baby corn cob was used for crop yield estimation. The water displacement method was employed to measure the actual volume of cobs, which was then compared with the volumes estimated using the developed mathematical models. For both cycles, similar trends were observed in both estimated and actual volumes of cobs, providing numerical confirmation for the validity of the developed mathematical models. The theoretical validity of these models was also established using statistical measures such as R2, adjusted R2, F-test, P-value, and correlation coefficient. Any deviations between estimated and actual volumes would indicate changes in the dependent variables of the model, attributed to the effects of climate change, as other internal and external factors are held constant. These models offer a critical predictive tool for stakeholders, enabling improved yield predictions and optimized resource allocation. As a result, they facilitate strategic planning for increased profitability.

Simple linear functional Errors–In–Variables models with correlated errors

This paper establishes a new estimator for simple linear measurement error models with correlated errors. Under a small-noise asymptotic regime and using perturbation theory, an unbiased estimator has been developed. The consistency and the asymptotic normality of the new estimator have been established and then validated by using synthetic data. Moreover, the confidence intervals of the slope have been revisited. Simulation results show that our estimator is certainly the most stable of existing methods and its performance exceeds that of other existing methods in terms of coverage and interval length.