Estimation of the intercept parameter for linear regression model with uncertain non-sample prior information

This paper considers alternative estimators of the intercept parameter of the linear regression model with normal error when uncertain non-sample prior information about the value of the slope parameter is available. The maximum likelihood, restricted, preliminary test and shrinkage estimators are considered. Based on their quadratic biases and mean square errors the relative performances of the estimators are investigated. Both analytical and graphical comparisons are explored. None of the estimators is found to be uniformly dominating the others. However, if the non-sample prior information regarding the value of the slope is not too far from its true value, the shrinkage estimator of the intercept parameter dominates the rest of the estimators.     

Supplier Strategies and Routines for Capability Development: Implications for Upgrading

This paper examines the strategies and routines adopted by small and medium-sized suppliers to develop capabilities that enable them to engage in upgrading, despite a precarious relational and institutional context. To this end, we investigate the strategic behaviour of two Bangladeshi garment manufacturers. Both started out as small suppliers for multinational enterprises (MNEs) and have eventually grown into micro-multinationals. The firms are involved in ‘tacit promissory contracting’ with their buyers, a specific form of international outsourcing relationship. The study adopts a multiple case study design that involves interviews with managers/owners of the firms. The analysis yields two key findings. Both firms have devised strategies and taken coherent routines involving actions to develop skills and motivation needed to perform appropriate functional activities (i.e. pre-production, production and post-production) as they embarked on different stages of upgrading. Furthermore, firms have designed routines to internalise the challenges originating from their relationships with their buyers and the institutional environment at the time that had the potential to affect their upgrading goals. The paper contributes to IB studies by highlighting how suppliers, even in a precarious context, can control their own strategies and routines, so as to develop capabilities that allow them to gradually redress the power imbalance between themselves and their buyers.     

The Implications of Change in Population Size,Distribution, and Composition on the Number of Overweight and Obese Adults and the Direct and Indirect Cost Associated with Overweight and Obese Adults in Texas Through 2040

Changes in population characteristics are associated with changes in the prevalence of physical and related health conditions with alternative types of population change leading to change in the prevalence of certain conditions. Examination of the effects of future demographic change on such conditions is, in turn, critical for understanding the future need for various types of health-related facilities and services. This article provides an example of how future demographic changes are likely to impact overweight and obese status in Texas, a rapidly growing and diversifying state. Specifically it uses population decomposition techniques to examine the relative impacts of population growth, aging and changes in the racial/ethnic composition of the population on increases in the prevalence and related costs of overweight and obesity in Texas, an important input for the formulation of statewide health policies. The number of overweight adults in Texas is projected to increase from 5.5 million in 2000 to 16.0 million in 2040, and the number of obese adults to increase from 3.5 million in 2000 to 14.6 million in 2040. The largest projected increases occur among Hispanics and other minority populations and for all race/ethnicity groups the increases are largest among those who are 65 years of age and older. Decomposition analysis indicates that of the projected increase of 10.5 million overweight adults from 2000 to 2040, 54.0% is attributable to population increase, 15.0% to change in age distribution, and 31.0% to change in racial/ethnic composition. Of the projected increase in the number of obese adults, 61.6% is due to population change and 38.4% to change in racial/ethnic composition. The annual costs associated with overweight and obesity prevalence are expected to increase from $10.5 billion in 2000 to $10.5 billion in 2000 to 40.3 billion in 2040. The results suggest that services to address these conditions will need to be widely dispersed across the state with particular concentrations of the elderly, Hispanics, and Other minority populations.     

Predictive assessment of copula models

Copulas are powerful explanatory tools for studying dependence patterns in multivariate data. While the primary use of copula models is in multivariate dependence modelling, they also offer predictive value for regression analysis. This article investigates the utility of copula models for model‐based predictions from two angles. We assess whether, where, and by how much various copula models differ in their predictions of a conditional mean and conditional quantiles. From a model selection perspective, we then evaluate the predictive discrepancy between copula models using in‐sample and out‐of‐sample predictions both in bivariate and higher‐dimensional settings. Our findings suggest that some copula models are more difficult to distinguish in terms of their overall predictive power than others, and depending on the quantity of interest, the differences in predictions can be detected only in some targeted regions. The situations where copula‐based regression approaches would be advantageous over traditional ones are discussed using simulated and real data. The Canadian Journal of Statistics 47: 8–26; 2019 © 2018 Statistical Society of Canada     

A Better Alternative to Non-parametric Approaches for Adjusting for Covariate Measurement Errors in Logistic Regression

In this article, we propose a flexible parametric (FP) approach for adjusting for covariate measurement errors in regression that can accommodate replicated measurements on the surrogate (mismeasured) version of the unobserved true covariate on all the study subjects or on a sub-sample of the study subjects as error assessment data. We utilize the general framework of the FP approach proposed by Hossain and Gustafson in 2009 for adjusting for covariate measurement errors in regression. The FP approach is then compared with the existing non-parametric approaches when error assessment data are available on the entire sample of the study subjects (complete error assessment data) considering covariate measurement error in a multiple logistic regression model. We also developed the FP approach when error assessment data are available on a sub-sample of the study subjects (partial error assessment data) and investigated its performance using both simulated and real life data. Simulation results reveal that, in comparable situations, the FP approach performs as good as or better than the competing non-parametric approaches in eliminating the bias that arises in the estimated regression parameters due to covariate measurement errors. Also, it results in better efficiency of the estimated parameters. Finally, the FP approach is found to perform adequately well in terms of bias correction, confidence coverage, and in achieving appropriate statistical power under partial error assessment data.     

Response-based multiple imputation method for minimizing the impact of covariate detection limit in logistic regression

Abstract

Presence of detection limit (DL) in covariates causes inflated bias and inaccurate mean squared error to the estimators of the regression parameters. This paper suggests a response-driven multiple imputation method to correct the deleterious impact introduced by the covariate DL in the estimators of the parameters of simple logistic regression model. The performance of the method has been thoroughly investigated, and found to outperform the existing competing methods. The proposed method is computationally simple and easily implementable by using three existing R libraries. The method is robust to the violation of distributional assumption for the covariate of interest.     

Determinants of 1980 to 1990 Net Migration in Texas Counties: The Role of Sustenance Specialization and Dominance in International Ecosystems

Abstract Patterns of 1980–1990 migration differ markedly from those of the 1970s but they have received little conceptual or empirical analysis because of limited data and because their diversity has resisted theoretical generalization. An expanded human ecological perspective incorporating international dimensions is hypothesized to explain differentials in migration during the 1980s. Counties with key functions which operate in international ecosystems in which the United States is dominant are hypothesized to control more resources and to have the highest net inmigration. Hypotheses are tested using regression analyses of 1980–1990 net migration for Texas' 254 counties. The results generally confirm that key functional activities were related to migration but less so in nonmetropolitan areas. Implications for rural development and for further research are discussed.     

THE EFFICIENCY OF THE COCHRANE-ORCUTT PROCEDURE

It is well known that the ordinary least squares (OLS) estimator, though unbiased, is inefficient in the presence of autocorrelated disturbances. Further, it is also widely accepted that the Cochrane-Orcutt (C-O) estimator is more efficient than the OLS estimator. However, Kadiyala (1968) and Maeshiro (1976, 1978) have argued that OLS is more efficient than C-O when the independent variable is trended and the autocorrelation coefficient is positive. We re-examine this issue and show that C-O is more efficient than OLS for the model without an intercept term.     

Shrinkage estimator of regression model under asymmetric loss

This article investigates the performance of the shrinkage estimator (SE) of the parameters of a simple linear regression model under the LINEX loss criterion. The risk function of the estimator under the asymmetric LINEX loss is derived and analyzed. The moment-generating functions and the first two moments of the estimators are also obtained. The risks of the SE have been compared numerically with that of pre-test and least-square estimators (LSEs) under the LINEX loss criterion. The numerical comparison reveals that under certain conditions the LSE is inadmissible, and the SE is the best among the three estimators.     

Nonmetropolitan Residential Segregation Revisited

Abstract Nonmetropolitan (nonmetro) residential segregation in 1990 and change in the preceding decade have received insufficient attention. A set of empirical hypotheses are derived and assessed using nonmetro and metropolitan (metro) counties in Texas. Places in nonmetro counties were more segregated than places in metro counties in 1990 as in 1980. Substantial declines in segregation occurred in both nonmetro and metro places but were largest in growing places in nonmetro counties. An analysis controlling for other determinants of segregation supports the premise that population change was a major determinant of 1980–1990 change in segregation. Implications for nonmetro areas in the 1990s are discussed.