Semiparametric estimation of logistic regression model with missing covariates and outcome

Lee, Shen Ming; Li, Chin‐Shang; Hsieh, Shu Hui; Huang, Liying

doi:10.1007/s00184-011-0345-9

Cited by 16 publications

(15 citation statements)

References 18 publications

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“…For the Poisson regression model (M 2 ) and the Negative binomial regression model (M 3 ), we also see the statistical signicance of estimated coecients based on P-values are very small (≈ 0). The two factors X and Z with positive coecients imply that they increase the incidence rate (see µ in (11) and 12) of number of trac violations while W makes it to be decreasing as in the case of ZIP model, see Tab. 3 and 4.…”

Section: Speed Regulationmentioning

confidence: 99%

“…Hsieh et al [9] extended method of Wang et al (2002) to introduce a semiparametric analysis of randomized response data with missing covariates in logistic regression. Lee et al [11] also extended method in Wang et al (2002) to present a semiparametric estimation of logistic regression model with missing covariates and outcome. Pho et al [30] discussed about three ubiquitous approaches to handle the issues having missing data.…”

Section: Speed Regulationmentioning

confidence: 99%

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Comparison among Akaike Information Criterion, Bayesian Information Criterion and Vuong's test in Model Selection: A Case Study of Violated Speed Regulation in Taiwan

Pho

et al. 2019

J. ADV. ENG. COMPUT.

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When doing research on scientific issues, it is very significant if our research issues are closely connected to real applications. In reality, when analyzing data in practice, there are frequently several models that can appropriate to the survey data. Hence, it is necessary to have a standard criterion to choose the most ecient model. In this article, our primary interest is to compare and discuss about the criteria for selecting a model and its applications. The authors provide approaches and procedures of these methods and apply to the traffic violation data where we look for the most appropriate model among Poisson regression, Zero-inflated Poisson regression and Negative binomial regression to capture between number of violated speed regulations and some factors including distance covered, motorcycle engine and age of respondents by using AIC, BIC and Vuong's test. Based on results on the training, validation and test data set, we find that the criteria AIC and BIC are more consistent and robust performance in model selection than the Vuong's test. In the present paper, the authors also discuss about advantages and disadvantages of these methods and provide some of the suggestions with potential directions in future research. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

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Section: Speed Regulationmentioning

confidence: 99%

Section: Speed Regulationmentioning

confidence: 99%

Comparison among Akaike Information Criterion, Bayesian Information Criterion and Vuong's test in Model Selection: A Case Study of Violated Speed Regulation in Taiwan

Pho

et al. 2019

J. ADV. ENG. COMPUT.

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“…About this regard, there are several scholars utilized the Newton-Raphson method to find parameters in the regression models with missing covariates data. Readers may refer to Hsieh et al (2009Hsieh et al ( , 2010, Lee et al (2012Lee et al ( , 2016, Lukusa et al (2016), Wang et al (2002) for more information. In addition, the Newton-Raphson method can work well in the data set having missing values, where some available function in the statistical software is unworkable (see e.g.…”

Section: Iterationmentioning

confidence: 99%

“…Lukusa et al (2016) employ the N-R method to find the optimization in the zero-inflated Poisson (ZIP) regression model with missing covariates. Other papers using this method include Hiesh et al (2009 and, Lee et al (2012 and. Readers may refer to Pho and Nguyen (2018) for the detail of the algorithm and applications of the N-R method.…”

Section: Introductionmentioning

confidence: 99%

Optimal Solution Techniques in Decision Sciences: A Review

Pho

Tran

et al. 2019

SSRN Journal

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Methods to find the optimization solution are fundamental and extremely crucial for scientists to program computational software to solve optimization problems efficiently and for practitioners to use it efficiently. Thus, it is very essential to know about the idea, origin, and usage of these methods. Although the methods have been used for very long time and the theory has been developed too long, most, if not all, of the authors who develop the theory are unknown and the theory has not been stated clearly and systematically. To bridge the gap in the literature in this area and provide academics and practitioners with an overview of the methods, this paper reviews and discusses the four most commonly used methods to find the optimization solution including the bisection, gradient, Newton-Raphson, and secant methods. We first introduce the origin and idea of the methods and develop all the necessary theorems to prove the existence and convergence of the estimate for each method.We then give two examples to illustrate the approaches. Thereafter, we review the literature of the applications of getting the optimization solutions in some important issues and discuss the advantages and disadvantages of each method. We note that all the theorems developed in our paper could be well-known but, so far, we have not seen any book or paper that discusses all the theorems stated in our paper in detail. Thus, we believe the theorems developed in our paper could still have some contributions to the literature. Our review is useful for academics and practitioners in finding the optimization solutions in their studies.

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“…Guo et al (2014) considered the estimation of a semi-parametric multi-index model using a weighted estimating equation approach. Lee et al (2012) considered logistic regression models with missing covariates and outcome. Efromovich (2012) dealt with adaptive orthogonal series estimators when the regression function belongs to a Sobolev class.…”

Section: Introductionmentioning

confidence: 99%

On histogram-based regression and classification with incomplete data

Eric

Mojirsheibani

2020

Metrika

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We consider the problem of nonparametric regression with possibly incomplete covariate vectors. The proposed estimators, which are based on histogram methods, are fully nonparametric and straightforward to implement. The presence of incomplete covariates is handled by an inverse weighting method, where the weights are estimates of the conditional probabilities of having incomplete covariate vectors. We also derive various exponential bounds on the L 1 norms of our estimators, which can be used to establish strong consistency results for the corresponding, closely related, problem of nonparametric classification with missing covariates. As the main focus and application of our results, we consider the problem of pattern recognition and statistical classification in the presence of incomplete covariates and propose histogram classifiers that are asymptotically optimal.

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Semiparametric estimation of logistic regression model with missing covariates and outcome

Cited by 16 publications

References 18 publications

Comparison among Akaike Information Criterion, Bayesian Information Criterion and Vuong's test in Model Selection: A Case Study of Violated Speed Regulation in Taiwan

Comparison among Akaike Information Criterion, Bayesian Information Criterion and Vuong's test in Model Selection: A Case Study of Violated Speed Regulation in Taiwan

Optimal Solution Techniques in Decision Sciences: A Review

On histogram-based regression and classification with incomplete data

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