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Shen-ming Lee - One of the best experts on this subject based on the ideXlab platform.

  • Logistic regression with outcome and covariates missing separately or simultaneously
    Computational Statistics & Data Analysis, 2013
    Co-Authors: Shu Hui Hsieh, Shen-ming Lee
    Abstract:

    Abstract Estimation methods are proposed for fitting logistic regression in which outcome and covariate variables are missing separately or simultaneously. One of the two proposed estimators is an extension of the validation Likelihood estimator of  Breslow and Cain (1988) . The other is a joint Conditional Likelihood estimator that uses both validation and non-validation data. Large sample properties of the proposed estimators are studied under certain regularity conditions. Simulation results show that the joint Conditional Likelihood estimator is more efficient than the validation Likelihood estimator, weighted estimator, and complete-case estimator. The practical use of the proposed methods is illustrated with data from a cable TV survey study in Taiwan.

  • Semiparametric estimation of logistic regression model with missing covariates and outcome
    Metrika, 2011
    Co-Authors: Shen-ming Lee, Shu Hui Hsieh, Li Hui Huang
    Abstract:

    We consider a semiparametric method to estimate logistic regression models with missing both covariates and an outcome variable, and propose two new estimators. The first, which is based solely on the validation set, is an extension of the validation Likelihood estimator of Breslow and Cain (Biometrika 75:11–20, 1988). The second is a joint Conditional Likelihood estimator based on the validation and non-validation data sets. Both estimators are semiparametric as they do not require any model assumptions regarding the missing data mechanism nor the specification of the Conditional distribution of the missing covariates given the observed covariates. The asymptotic distribution theory is developed under the assumption that all covariate variables are categorical. The finite-sample properties of the proposed estimators are investigated through simulation studies showing that the joint Conditional Likelihood estimator is the most efficient. A cable TV survey data set from Taiwan is used to illustrate the practical use of the proposed methodology.

  • Conditional Likelihood estimation and efficiency comparisons in proportional odds model with missing covariates
    Annals of the Institute of Statistical Mathematics, 2009
    Co-Authors: S. H. Hsieh, Shen-ming Lee, Pao-sheng Shen, M. F. Liu
    Abstract:

    Missing value, Proportional odds model, Ordinal categorical data, Conditional Likelihood,

  • Semiparametric analysis of randomized response data with missing covariates in logistic regression
    Computational Statistics & Data Analysis, 2009
    Co-Authors: S. H. Hsieh, Shen-ming Lee, Pao-sheng Shen
    Abstract:

    In this article, two semiparametric approaches are developed for analyzing randomized response data with missing covariates in logistic regression model. One of the two proposed estimators is an extension of the validation Likelihood estimator of Breslow and Cain [Breslow, N.E., and Cain, K.C. 1988. Logistic regression for two-stage case-control data. Biometrika. 75, 11-20]. The other is a joint Conditional Likelihood estimator based on both validation and non-validation data sets. We present a large sample theory for the proposed estimators. Simulation results show that the joint Conditional Likelihood estimator is more efficient than the validation Likelihood estimator, weighted estimator, complete-case estimator and partial Likelihood estimator. We also illustrate the methods using data from a cable TV study.

  • JOINT Conditional Likelihood ESTIMATOR IN LOGISTIC REGRESSION WITH MISSING COVARIATE DATA
    2002
    Co-Authors: C. Y. Wang, J. C. Chen, Shen-ming Lee
    Abstract:

    This article considers semiparametric estimation in logistic regression with missing covariates. In a validation subsample, we assume covariates are mea- sured without error. Some covariates are missing in the non-validation set, while surrogate variables may be available for all study subjects. We consider the case when a covariate variable is missing at random such that the selection probability of the validation set depends only on observed data. Breslow and Cain (1988) pro- posed a Conditional Likelihood approach based on the validation set. We combine the Conditional Likelihoods of the validation set and the non-validation set. The proposed estimator is easy to implement and is semiparametric since no additional model assumption is imposed. Large sample theory is developed. For the esti- mation of the parameter for the missing covariate, simulations show that, under various situations, the proposed estimator is significantly more efficient than the validation Likelihood estimator of Breslow and Cain and the inverse selection prob- ability weighted estimator. Under moderate sample sizes and moderate values of relative risk parameters, our estimator remains competitive when compared with the nonparametric maximum Likelihood estimator of Scott and Wild (1997). The proposed method is illustrated by a real data example.

Richard Huggins - One of the best experts on this subject based on the ideXlab platform.

  • the vgam package for capture recapture data using the Conditional Likelihood
    Journal of Statistical Software, 2015
    Co-Authors: Thomas W Yee, Jakub Stoklosa, Richard Huggins
    Abstract:

    It is well known that using individual covariate information (such as body weight or gender) to model heterogeneity in capture-recapture (CR) experiments can greatly enhance inferences on the size of a closed population. Since individual covariates are only observable for captured individuals, complex Conditional Likelihood methods are usually required and these do not constitute a standard generalized linear model (GLM) family. Modern statistical techniques such as generalized additive models (GAMs), which allow a relaxing of the linearity assumptions on the covariates, are readily available for many standard GLM families. Fortunately, a natural statistical framework for maximizing Conditional Likelihoods is available in the Vector GLM and Vector GAM classes of models. We present several new R functions (implemented within the VGAM package) specifically developed to allow the incorporation of individual covariates in the analysis of closed population CR data using a GLM/GAM-like approach and the Conditional Likelihood. As a result, a wide variety of practical tools are now readily available in the VGAM object oriented framework. We discuss and demonstrate their advantages, features and flexibility using the new VGAM CR functions on several examples.

  • a review of the use of Conditional Likelihood in capture recapture experiments
    International Statistical Review, 2011
    Co-Authors: Richard Huggins, Wen-han Hwang
    Abstract:

    Resume Nous presentons une perspective moderne de l'approche par vraisemblances conditionnelles de l'analyse des experiences de capture-recapture. Nous montrons que ces vraisemblances conditionnelles relevent d'un modele lineaire generalise, ce qui permet l'application des nombreuses methodes elaborees dans ce cadre. Pour replacer ces applications dans leur contexte, nous passons en revue quelques-unes des approches existantes dans les modeles de capture-recapture avec probabilites de capture heterogenes au sein de populations fermees. Nous decrivons, en particulier, l'utilisation de modeles de melange parametriques et non parametriques, et examinons de facon plus detaillee le cas ou les probabilites de capture sont fonction de covariables. Summary We present a modern perspective of the Conditional Likelihood approach to the analysis of capture-recapture experiments, which shows the Conditional Likelihood to be a member of generalized linear model (GLM). Hence, there is the potential to apply the full range of GLM methodologies. To put this method in context, we first review some approaches to capture-recapture experiments with heterogeneous capture probabilities in closed populations, covering parametric and non-parametric mixture models and the use of covariates. We then review in more detail the analysis of capture-recapture experiments when the capture probabilities depend on a covariate.

  • A Review of the Use of Conditional Likelihood in Capture-Recapture Experiments
    International Statistical Review, 2011
    Co-Authors: Richard Huggins, Wen-han Hwang
    Abstract:

    We present a modern perspective of the Conditional Likelihood approach to the analysis of capture-recapture experiments, which shows the Conditional Likelihood to be a member of generalized linear model (GLM). Hence, there is the potential to apply the full range of GLM methodologies. To put this method in context, we first review some approaches to capture-recapture experiments with heterogeneous capture probabilities in closed populations, covering parametric and non-parametric mixture models and the use of covariates. We then review in more detail the analysis of capture-recapture experiments when the capture probabilities depend on a covariate

  • Some practical aspects of a Conditional Likelihood approach to capture experiments
    Biometrics, 1991
    Co-Authors: Richard Huggins
    Abstract:

    The use of Conditional Likelihood methods in the analysis of capture data allows the modeling of capture probabilities in terms of observable characteristics of the captured individuals and the trapping occasions. The resulting models may then be used to estimate the size of the population. Here the use of Conditional Likelihood procedures to construct models for capture probabilities is discussed and illustrated by an example.

S. H. Hsieh - One of the best experts on this subject based on the ideXlab platform.

Pao-sheng Shen - One of the best experts on this subject based on the ideXlab platform.

Brisa N. Sánchez - One of the best experts on this subject based on the ideXlab platform.

  • Fitting stratified proportional odds models by amalgamating Conditional Likelihoods.
    Statistics in medicine, 2008
    Co-Authors: Bhramar Mukherjee, Jaeil Ahn, Ivy Liu, Paul J. Rathouz, Brisa N. Sánchez
    Abstract:

    Classical methods for fitting a varying intercept logistic regression model to stratified data are based on the Conditional Likelihood principle to eliminate the stratum-specific nuisance parameters. When the outcome variable has multiple ordered categories, a natural choice for the outcome model is a stratified proportional odds or cumulative logit model. However, classical conditioning techniques do not apply to the general K-category cumulative logit model (K>2) with varying stratum-specific intercepts as there is no reduction due to sufficiency; the nuisance parameters remain in the Conditional Likelihood. We propose a methodology to fit stratified proportional odds model by amalgamating Conditional Likelihoods obtained from all possible binary collapsings of the ordinal scale. The method allows for categorical and continuous covariates in a general regression framework. We provide a robust sandwich estimate of the variance of the proposed estimator. For binary exposures, we show equivalence of our approach to the estimators already proposed in the literature. The proposed recipe can be implemented very easily in standard software. We illustrate the methods via three real data examples related to biomedical research. Simulation results comparing the proposed method with a random effects model on the stratification parameters are also furnished.