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Full-Text Articles in Social and Behavioral Sciences
Enriching Surveys With Supplementary Data And Its Application To Studying Wage Regression, Denis H. Y. Leung, Ken Yamada, Biao Zhang
Enriching Surveys With Supplementary Data And Its Application To Studying Wage Regression, Denis H. Y. Leung, Ken Yamada, Biao Zhang
Research Collection School Of Economics
We consider the problem of supplementing survey data with additional information from a population. The framework we use is very general; examples are missing data problems, measurement error models and combining data from multiple surveys. We do not require the survey data to be a simple random sample of the population of interest. The key assumption we make is that there exists a set of common variables between the survey and the supplementary data. Thus, the supplementary data serve the dual role of providing adjustments to the survey data for model consistencies and also enriching the survey data for improved …
Enriching Surveys With Supplementary Data And Its Application To Studying Wage Regression, Denis H. Y. Leung, Ken Yamada, Biao Zhang
Enriching Surveys With Supplementary Data And Its Application To Studying Wage Regression, Denis H. Y. Leung, Ken Yamada, Biao Zhang
Research Collection School Of Economics
We consider the problem of supplementing survey data with additional information from a population. The framework we use is very general; examples are missing data problems, measurement error models and combining data from multiple surveys. We do not require the survey data to be a simple random sample of the population of interest. The key assumption we make is that there exists a set of common variables between the survey and the supplementary data. Thus, the supplementary data serve the dual role of providing adjustments to the survey data for model consistencies and also enriching the survey data for improved …
Semiparametric Analysis In Conditionally Independent Multivariate Mixture Models, T. Wrobel, Denis H. Y. Leung, J. Qin, T. Hettmansperger
Semiparametric Analysis In Conditionally Independent Multivariate Mixture Models, T. Wrobel, Denis H. Y. Leung, J. Qin, T. Hettmansperger
Research Collection School Of Economics
The conditional independence assumption is commonly used in multivariate mixture models in behavioral research. We propose an exponential tilt model to analyze data from a multivariate mixture distribution with conditionally independent components. In this model, the log ratio of the density functions of the components is modeled as a quadratic function in the observations. There are a number of advantages in this approach. First, except for the exponential tilt assumption, the marginal distributions of the observations can be completely arbitrary. Second, unlike some previous methods, which require the multivariate data to be discrete, modeling can be performed based on the …
Testing Conditional Independence Via Empirical Likelihood, Liangjun Su, Halbert White
Testing Conditional Independence Via Empirical Likelihood, Liangjun Su, Halbert White
Research Collection School Of Economics
We construct two classes of smoothed empirical likelihood ratio tests for the conditional independence hypothesis by writing the null hypothesis as an infinite collection of conditional moment restrictions indexed by a nuisance parameter. One class is based on the CDF; another is based on smoother functions. We show that the test statistics are asymptotically normal under the null hypothesis and a sequence of Pitman local alternatives. We also show that the tests possess an asymptotic optimality property in terms of average power. Simulations suggest that the tests are well behaved in finite samples. Applications to some economic and financial time …
Shrinkage Empirical Likelihood Estimator In Longitudinal Analysis With Time-Dependent Covariates: Application To Modeling The Health Of Filipino Children, Denis H. Y. Leung, Dylan S. Small, Jing Qin, Min Zhu
Shrinkage Empirical Likelihood Estimator In Longitudinal Analysis With Time-Dependent Covariates: Application To Modeling The Health Of Filipino Children, Denis H. Y. Leung, Dylan S. Small, Jing Qin, Min Zhu
Research Collection School Of Economics
The method of generalized estimating equations (GEE) is a popular tool for analysing longitudinal (panel) data. Often, the covariates collected are time-dependent in nature, for example, age, relapse status, monthly income. When using GEE to analyse longitudinal data with time-dependent covariates, crucial assumptions about the covariates are necessary for valid inferences to be drawn. When those assumptions do not hold or cannot be verified, Pepe and Anderson (1994, Communications in Statistics, Simulations and Computation 23, 939–951) advocated using an independence working correlation assumption in the GEE model as a robust approach. However, using GEE with the independence correlation assumption may …
Testing Conditional Independence Via Empirical Likelihood, Liangjun Su, Halbert White
Testing Conditional Independence Via Empirical Likelihood, Liangjun Su, Halbert White
Research Collection School Of Economics
We construct two classes of smoothed empirical likelihood ratio tests for the conditional independence hypothesis by writing the null hypothesis as an infinite collection of conditional moment restrictions indexed by a nuisance parameter. One class is based on the CDF; another is based on smoother functions. We show that the test statistics are asymptotically normal under the null hypothesis and a sequence of Pitman local alternatives. We also show that the tests possess an asymptotic optimality property in terms of average power. Simulations suggest that the tests are well behaved in finite samples. Applications to some economic and financial time …
Tilted Nonparametric Estimation Of Volatility Functions With Empirical Applications, Ke-Li Xu, Peter C. B. Phillips
Tilted Nonparametric Estimation Of Volatility Functions With Empirical Applications, Ke-Li Xu, Peter C. B. Phillips
Research Collection School Of Economics
This article proposes a novel positive nonparametric estimator of the conditional variance function without reliance on logarithmic or other transformations. The estimator is based on an empirical likelihood modification of conventional local-level nonparametric regression applied to squared residuals of the mean regression. The estimator is shown to be asymptotically equivalent to the local linear estimator in the case of unbounded support but, unlike that estimator, is restricted to be nonnegative in finite samples. It is fully adaptive to the unknown conditional mean function. Simulations are conducted to evaluate the finite-sample performance of the estimator. Two empirical applications are reported. One …
Empirical Likelihood In Missing Data Problems, Jing Qin, Biao Zhang, Denis H. Y. Leung
Empirical Likelihood In Missing Data Problems, Jing Qin, Biao Zhang, Denis H. Y. Leung
Research Collection School Of Economics
Missing data is a ubiquitous problem in medical and social sciences. It is well known that inferences based only on the complete data may not only lose efficiency, but may also lead to biased results if the data is not missing completely at random (MCAR). The inverse-probability weighting method proposed by Horvitz and Thompson (1952) is a popular alternative when the data is not MCAR. The Horvitz–Thompson method, however, is sensitive to the inverse weights and may suffer from loss of efficiency. In this paper, we propose a unified empirical likelihood approach to missing data problems and explore the use …
Efficient Parameter Estimation In Longitudinal Data Analysis Using A Hybrid Gee Method, Denis H. Y. Leung, You Gan Wang, Min Zhu
Efficient Parameter Estimation In Longitudinal Data Analysis Using A Hybrid Gee Method, Denis H. Y. Leung, You Gan Wang, Min Zhu
Research Collection School Of Economics
The method of generalized estimating equations (GEEs) provides consistent estimates of the regression parameters in a marginal regression model for longitudinal data, even when the working correlation model is misspecified (Liang and Zeger, 1986). However, the efficiency of a GEE estimate can be seriously affected by the choice of the working correlation model. This study addresses this problem by proposing a hybrid method that combines multiple GEEs based on different working correlation models, using the empirical likelihood method (Qin and Lawless, 1994). Analyses show that this hybrid method is more efficient than a GEE using a misspecified working correlation model. …
Improving Semiparametric Estimation By Using Surrogate Data, Song Xi Chen, Leung, Denis H. Y., Jin Qin
Improving Semiparametric Estimation By Using Surrogate Data, Song Xi Chen, Leung, Denis H. Y., Jin Qin
Research Collection School Of Economics
The paper considers estimating a parameter beta that defines an estimating function U(y, x, beta) for an outcome variable y and its covariate x when the outcome is missing in some of the observations. We assume that, in addition to the outcome and the covariate, a surrogate outcome is available in every observation. The efficiency of existing estimators for beta depends critically on correctly specifying the conditional expectation of U given the surrogate and the covariate. When the conditional expectation is not correctly specified, which is the most likely scenario in practice, the efficiency of estimation can be severely compromised …
Analysing Survey Data With Incomplete Responses By Using A Method Based On Empirical Likelihood, Denis H. Y. Leung, Jing Qin
Analysing Survey Data With Incomplete Responses By Using A Method Based On Empirical Likelihood, Denis H. Y. Leung, Jing Qin
Research Collection School Of Economics
In many surveys, missing response is a common problem. As an example, Zahner, Jacobs, Freeman and Trainor analysed data from a study of child psychopathology in the State of Connecticut, USA. In that study, the response variable, psychopathology, was inferred from questions that were addressed to teachers of the children and was subject to a high level of missingness. However, the missing responses were supplemented by surrogate information that was provided by the parents and/or the primary care providers of the children. In such a situation, it is conceivable that the supplemental information can be used to recover some of …
Semi-Parametric Inference In A Bivariate (Multivariate) Mixture Model, Denis H. Y. Leung, Jing Qin
Semi-Parametric Inference In A Bivariate (Multivariate) Mixture Model, Denis H. Y. Leung, Jing Qin
Research Collection School Of Economics
We consider estimation in a bivariate mixture model in which the component distributions can be decomposed into identical distributions. Previous approaches to estimation involve parametrizing the distributions. In this paper, we use a semi-parametric approach. The method is based on the exponential tilt model of Anderson (1979), where the log ratio of probability (density) functions from the bivariate components is linear in the observations. The proposed model does not require training samples, i.e., data with confirmed component membership. We show that in bivariate mixture models, parameters are identifiable. This is in contrast to previous works, where parameters are identifiable if …
A Semi-Parametric Two-Component Compound Mixture Model And Its Application To Estimating Malaria Attributable Fractions, Jing Qin, Denis H. Y. Leung
A Semi-Parametric Two-Component Compound Mixture Model And Its Application To Estimating Malaria Attributable Fractions, Jing Qin, Denis H. Y. Leung
Research Collection School Of Economics
Malaria remains a major epidemiologic problem in many developing countries. Malaria is defined as the presence of parasites and symptoms (usually fever) due to the parasites. In endemic areas, an individual may have symptoms attributable either to malaria or to other causes. From a clinical viewpoint, it is important to correctly diagnose an individual who has developed symptoms so that the appropriate treatments can be given. From an epidemiologic and economic viewpoint, it is important to determine the proportion of malaria-affected cases in individuals who have symptoms so that policies on intervention program can be developed. Once symptoms have developed …
Information Recovery In A Study With Surrogate Endpoints, Song Xi Chen, Denis H. Y. Leung, Jing Qin
Information Recovery In A Study With Surrogate Endpoints, Song Xi Chen, Denis H. Y. Leung, Jing Qin
Research Collection School Of Economics
Recently, there has been a lot of interest in statistical methods for analyzing data with surrogate endpoints. In this article, we consider parameter estimation from a model that relates a variable Y to a set of covariates, X, in the presence of a surrogate, S. We assume that the data are made up of two random samples from the population, a validation set where (Y, X, S) are observed on every subject and a nonvalidation set where only (X, S) are measured. We show how information from the nonvalidation set can be incorporated to improve upon estimation of a parameter …