• Journal of the Korean Data and Information Science Society
• /
• v.26 no.1
• /
• pp.209-216
• /
• 2015

Quantile regression models with covariate measurement errors have received a great deal of attention in both the theoretical and the applied statistical literature. A lot of effort has been devoted to develop effective estimation methods for such quantile regression models. In this paper we propose the partially linear support vector orthogonal quantile regression model in the presence of covariate measurement errors. We also provide a generalized approximate cross-validation method for choosing the hyperparameters and the ratios of the error variances which affect the performance of the proposed model. The proposed model is evaluated through simulations.

• The Korean Journal of Applied Statistics
• /
• v.22 no.3
• /
• pp.607-616
• /
• 2009

In many epidemiological studies, the occurrence times of the event of interest are right-censored or interval censored. In certain situations such as the AIDS data, however, the incubation period which is the time between HIV infection and the diagnosis of AIDS is usually doubly censored. In this paper, we impute the interval censored HIV infection time using three imputation methods. Mid imputation, conditional mean imputation and approximate Bayesian bootstrap are implemented to obtain right censored data, and then Gibbs sampler is used to estimate the coefficient factor of the incubation period. By using Bayesian approach, flexible modeling and the use of prior information is available. We applied both parametric and semi-parametric methods for estimating the effect of the covariate and compared the imputation results incorporating prior information for the covariate effects.

• Journal of the Korean Statistical Society
• /
• v.24 no.2
• /
• pp.537-549
• /
• 1995

In this article, we propose a class of weighted estimators for the excess risk in additive risk model with a binary covariate. The proposed estimator is consistent and asymptotically normal. When the assumed model is inappropriate, however, the estimators with different weights converge to nonidentical constants. This fact enables us to develop a goodness-of-fit test for the excess assumption by comparing estimators with diffrent weights. It is shown that the proposed test converges in distribution to normal with mean zero and is consistent under the model misspecifications. Furthermore, the finite-sample properties of the proposed test procedure are investigated and two examples using real data are presented.

• The Korean Journal of Applied Statistics
• /
• v.9 no.1
• /
• pp.75-89
• /
• 1996

This paper proposes a goodness-of-fit test for checking the adequacy of the additive risk model with a binary covariate. The test statistic is based on martingale residuals, which is the extended form of Wei(1984)'s test. The proposed test is shown to be consistent and asymptotically normally distributed under the regularity conditions. Furthermore, the test procedure is illustrated with two set of real data and the results are discussed.

• Communications for Statistical Applications and Methods
• /
• v.22 no.4
• /
• pp.349-359
• /
• 2015

We study a semiparametric Bayesian approach to small area estimation under a nested error linear regression model with area level covariate subject to measurement error. Consideration is given to radial basis functions for the regression spline and knots on a grid of equally spaced sample quantiles of covariate with measurement errors in the nested error linear regression model setup. We conduct a hierarchical Bayesian structural measurement error model for small areas and prove the propriety of the joint posterior based on a given hierarchical Bayesian framework since some priors are defined non-informative improper priors that uses Markov Chain Monte Carlo methods to fit it. Our methodology is illustrated using numerical examples to compare possible models based on model adequacy criteria; in addition, analysis is conducted based on real data.

• Communications for Statistical Applications and Methods
• /
• v.31 no.1
• /
• pp.155-178
• /
• 2024

Regression discontinuity (RD) design is one of the most widely used methods in causal inference for estimation of treatment effect when the treatment is created by a cutpoint from the covariate of interest. There has been little attention to RD design, although it provides a very useful tool for analysis of treatment effect for censored data. In this paper, we define the causal effect for survival function in RD design when the treatment is assigned deterministically by the covariate of interest. We propose estimators of this causal effect for survival data by using transformation, which leads unbiased estimator of the survival function with local linear regression. Simulation studies show the validity of our approach. We also illustrate our proposed method using the prostate, lung, colorectal and ovarian (PLCO) dataset.

• The Korean Journal of Applied Statistics
• /
• v.29 no.5
• /
• pp.849-858
• /
• 2016

It is important to control confounding bias when estimating the causal effect of treatment in an observational study. We illustrated that the covariate selection in the causal inference is different from the variable selection in the ANCOVA model. We then investigated the three criteria of covariate selection for controlling confounding bias, which can be used when we have inadequate information to draw a complete causal graph. VanderWeele and Shpitser (2011) proposed one of them and claimed it was better than the other two. We show by example that their criterion also has limitations and some disadvantages. There is no clear winner; however, their criterion is better (if some correction is made on its condition) than the other two because it can remove the confounding bias.

• Asian-Australasian Journal of Animal Sciences
• /
• v.15 no.8
• /
• pp.1080-1084
• /
• 2002

Carcass characteristics of 241 crossbred pigs (Korean wild boars ${\times}$ Landrace sows) were analyzed to examine variations in fasted body weight (FASTWT), carcass weight (CARCWT), dressing percentage (DP), back fat thickness (BFT) and longissimus muscle weight (LMW), and to estimate genetic and phenotypic parameters using three different slaughter-end points. Covariates in the least squares full sib model were slaughter age, fasted body weight and back fat thickness of the carcass. Coefficient of variation was highest for BFT followed by LMW, CARCWT, FASTWT and DP in magnitude. Regressions of three covariates on traits were all linear. However, slaughter age was not significant as a linear covariate for five traits while FASTWT was significant for CARCWT and LMW and BFT was significant for all remaining traits. Genetic and phenotypic variation was considerably reduced by regressing FASTWT or BFT in the model. Heritability estimates of FASTWT, CARCWT, DP and BFT were 0.68, 0.61, 0.11 and 0.49, respectively, using slaughter age as covariate (model 1). Those of CARCWT, DP, BFT and LMW were 0.15, 0.15, 0.30 and 0.11, respectively, using FASTWT as covariate (model 2). Heritability estimates of the traits using LMW as covariate (model 3) were similar to the estimates from Model 1 except that the estimate of CARCWT was reduced to 0.39. Genetic or phenotypic correlations among FASTWT, CARCWT and BFT were all positive and moderate to high. Those between BFT and LMW were also positive and low to moderate. However, genetic and phenotypic correlations between DP and CARCWT were positive while those between DP and FASTWT were negative. It was suggested from this study that differences in carcass yield traits be determined using slaughter age or back fat thickness as slaughter-end point and carcass quality traits using fasted body weight as slaughter-end point.

• Communications for Statistical Applications and Methods
• /
• v.3 no.1
• /
• pp.61-71
• /
• 1996

Kim and Song(1995)과 Kim and Lee(1996)는 하나의 이지공변량(binary covariate)을 갖는 가산위험모형(additive risk model)의 적합도검정법(goodness-of-fit test)을 제안했다. 전자는 모수의 가중추정량들의 차에 기초한 검정법이며 후자는 마팅게일잔차(martingale residual)에 기초한 검정법이다. 본 논문에서는 모의실험을 통하여 두 검정법을 비교하였다.

• Genomics & Informatics
• /
• v.14 no.4
• /
• pp.160-165
• /
• 2016

Over the past decade, the detection of gene-gene interactions has become more and more popular in the field of genome-wide association studies (GWASs). The goal of the GWAS is to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single-nucleotide polymorphisms. However, such tests are computationally demanding and methodologically challenging. Recently, a simple but powerful method, named "BOolean Operation-based Screening and Testing" (BOOST), was proposed for genome-wide gene-gene interaction analyses. BOOST was designed with a Boolean representation of genotype data and is approximately equivalent to the log-linear model. It is extremely fast, and genome-wide gene-gene interaction analyses can be completed within a few hours. However, BOOST can not adjust for covariate effects, and its type-1 error control is not correct. Thus, we considered two-step approaches for gene-gene interaction analyses. First, we selected gene-gene interactions with BOOST and applied logistic regression with covariate adjustments to select gene-gene interactions. We applied the two-step approach to type 2 diabetes (T2D) in the Korea Association Resource (KARE) cohort and identified some promising pairs of single-nucleotide polymorphisms associated with T2D.