Pseudo-empirical Bayes estimation of small area means under a nested error linear regression model with functional measurement errors

Small area estimation is studied under a nested error linear regression model with area level covariate subject to measurement error. Ghosh and Sinha (2007) obtained a pseudo-Bayes (PB) predictor of a small area mean and a corresponding pseudo-empirical Bayes (PEB) predictor, using the sample means of the observed covariate values to estimate the true covariate values. In this paper, we first derive an efficient PB predictor by using all the available data to estimate true covariate values. We then obtain a corresponding PEB predictor and show that it is asymptotically “optimal”. In addition, we employ a jackknife method to estimate the mean squared prediction error (MSPE) of the PEB predictor. Finally, we report the results of a simulation study on the performance of our PEB predictor and associated jackknife MSPE estimator. Our results show that the proposed PEB predictor can lead to significant gain in efficiency over the previously proposed PEB predictor. Area level models are also studied.     

Overcoming biases and misconceptions in ecological studies

The aggregate data study design provides an alternative group level analysis to ecological studies in the estimation of individual level health risks. An aggregate model is derived by aggregating a plausible individual level relative rate model within groups, such that population-based disease rates are modelled as functions of individual level covariate data. We apply an aggregate data method to a series of fictitious examples from a review paper by Greenland and Robins which illustrated the problems that can arise when using the results of ecological studies to make inference about individual health risks. We use simulated data based on their examples to demonstrate that the aggregate data approach can address many of the sources of bias that are inherent in typical ecological analyses, even though the limited between-region covariate variation in these examples reduces the efficiency of the aggregate study. The aggregate method has the potential to estimate exposure effects of interest in the presence of non-linearity, confounding at individual and group levels, effect modification, classical measurement error in the exposure and non-differential misclassification in the confounder.     

论技术创新测度指标设置

本文依据技术创新活动的商业实质,从其活动过程的投入产出因素入手,阐述了测度技术创新水平的指标设置问题.     

Parameterization and inference for nonparametric regression problems

We consider local likelihood or local estimating equations, in which a multivariate function () is estimated but a derived function () of () is of interest. In many applications, when most naturally formulated the derived function is a non-linear function of (). In trying to understand whether the derived non-linear function is constant or linear, a problem arises with this approach: when the function is actually constant or linear, the expectation of the function estimate need not be constant or linear, at least to second order. In such circumstances, the simplest standard methods in nonparametric regression for testing whether a function is constant or linear cannot be applied. We develop a simple general solution which is applicable to nonparametric regression, varying-coefficient models, nonparametric generalized linear models, etc. We show that, in local linear kernel regression, inference about the derived function () is facilitated without a loss of power by reparameterization so that () is itself a component of (). Our approach is in contrast with the standard practice of choosing () for convenience and allowing ()> to be a non-linear function of (). The methods are applied to an important data set in nutritional epidemiology.     

The cross-country and cross-time measurement invariance of positive and negative affect scales: Evidence from European Social Survey

Survey-based measures of subjective well-being are increasingly often analyzed cross-culturally. However, international comparison of these measures requires measurement invariance. Therefore, the major goal of this study is to investigate the cross-country and cross-time comparability of the 4-item positive and 7-item negative affect scales used in European Social Survey Round 3 (2006) and Round 6 (2012). This study applies both the traditional exact and the more recent Bayesian approximate approach to assess whether the affect scales are measurement invariant. The approximate approach detected several non-invariant items that are problematic for cross-national comparison and should be dropped from the scales. Consequently, measurement invariance was established in all countries over the two rounds for the reduced scales, allowing researchers to meaningfully compare their latent mean scores and the relationships with other theoretical constructs of interest. Thus, the study highlights the advantages of using multiple indicators and the necessity of measurement invariance testing in subjective well-being research.     

互感电桥法测量超导临界温度

本文描述了超导体临界温度的一种测量方法方.系统使用相敏深测,仪器成深头形式,可以直接插入杜瓦进行测量.该装置能测量小至12Oμg的超导体样品的临界温度,信噪比为10:1.     

Variable Selection for Semiparametric Varying Coefficient Partially Linear Errors-in-Variables (EV) Model with Missing Response

This paper focuses on the variable selection for semiparametric varying coefficient partially linear model when the covariates are measured with additive errors and the response is missing. An adaptive lasso estimator and the smoothly clipped absolute deviation estimator as a comparison for the parameters are proposed. With the proper selection of regularization parameter, the sampling properties including the consistency of the two procedures and the oracle properties are established. Furthermore, the algorithms and corresponding standard error formulas are discussed. A simulation study is carried out to assess the finite sample performance of the proposed methods.     

Confidence Interval Estimation in Ultrastructural Model

The present article proposes a methodology to construct confidence interval for slope parameter and joint confidence region for intercept term and slope parameter in an ultra-structural model with equation error and correlated measurement errors.     

A Note on the Dimensionality of Quality of Life Scales: An Illustration with the Satisfaction with Life Scale (SWLS)

A case is made that measures used in quality of life and happiness research will be essentially unidimensional: inherently tapping minor dimensions. This is illustrated using Diener’s Satisfaction with Life Scale (SWLS). It is shown that the SWLS does not meet the standard of strict unidimensionality, but that the interpretation of the total scale score is not compromised because the additional dimensions are relatively minor. In the context of the example, a multi-step strategy is described that allows researchers to test for essential unidimensionality. Throughout the article, essential unidimensionality is contrasted with the received view of strict unidimensionality and confirmatory factor analysis methods.

Estimation for varying coefficient partially nonlinear models with distorted measurement errors

In this paper, we propose a new varying coefficient partially nonlinear model where both the response and predictors are not directly observed, but are observed by unknown distorting functions of a commonly observable covariate. Because of the complexity of the model, existing estimation methods cannot be directly employed. For this, we propose using an efficient nonparametric regression to estimate the unknown distortion functions concerning the covariates and response on the distorting variable, and further, we obtain the profile nonlinear least squares estimators for the parameters and the coefficient functions using the calibrated variables. Furthermore, we establish the asymptotic properties of the resulting estimators. To illustrate our proposed methodology, we carry out some simulated and real examples.