• Journal of the Korean Data and Information Science Society
• /
• 제17권3호
• /
• pp.999-1007
• /
• 2006

The semiparametric gamma frailty models have been often used for multivariate survival analysis because they give an explicit marginal likelihood. The commonly used estimation procedure is the profile likelihood method based on marginal likelihood, which provides the same parameter estimates as the EM algorithm. In this paper we show in finite samples the standard profile-likelihood method can lead to an underestimation of parameters, particularly for the frailty parameter. To overcome this problem, we propose an adjusted profile-likelihood method. For the illustration a numerical example and a small-sample simulation study are presented.

• Asian-Australasian Journal of Animal Sciences
• /
• 제13권8호
• /
• pp.1035-1039
• /
• 2000

This study developed a Poisson generalized linear mixed model and a procedure to estimate genetic parameters for count traits. The method derived from a frequentist perspective was based on hierarchical likelihood, and the maximum adjusted profile hierarchical likelihood was employed to estimate dispersion parameters of genetic random effects. Current approach is a generalization of Henderson's method to non-normal data, and was applied to simulated data. Underestimation was observed in the genetic variance component estimates for the data simulated with large heritability by using the Poisson generalized linear mixed model and the corresponding maximum adjusted profile hierarchical likelihood. However, the current method fitted the data generated with small heritability better than those generated with large heritability.

• Journal of the Korean Data and Information Science Society
• /
• 제15권1호
• /
• pp.251-261
• /
• 2004

The paper focuses primarily on the standard linear multiple regression model where the parameter of interest is a ratio of two regression coefficients. The general model includes the calibration model, the Fieller-Creasy problem, slope-ratio assays, parallel-line assays, and bioequivalence. We provide an orthogonal transformation (cf. Cox and Reid (1987)) of the original parameter vector. Also, we give some remarks on the difficulties associated with likelihood based confidence interval.

• Communications for Statistical Applications and Methods
• /
• 제7권2호
• /
• pp.481-487
• /
• 2000

When we construct an interval estimate of two independent proportions with rare events, the standard approach based on the normal approximation behaves badly in many cases. The problem becomes more severe when no success observations are observed on both groups. In this paper, we compare two alternative methods of constructing a confidence interval of the difference of two independent proportions by use of simulation. One is based on the profile likelihood and the other is the Bayesian probability interval. It is shown in this paper that the Bayesian interval estimator is easy to be implemented and performs almost identical to the best frequentist's method -the profile likelihood approach.

• Communications for Statistical Applications and Methods
• /
• 제23권4호
• /
• pp.321-334
• /
• 2016

In this paper, we propose power t distribution based on t distribution. We also study the properties of and inferences for power t model in order to solve the problem of real data showing both skewness and heavy tails. The comparison of skew t and power t distributions is based on density plots, skewness and kurtosis. Note that, at the given degree of freedom, the kurtosis's range of the power t model surpasses that of the skew t model at all times. We draw inferences for two parameters of the power t distribution and four parameters of the location-scale extension of power t distribution via maximum likelihood. The Fisher information matrix derived is nonsingular on the whole parametric space; in addition we obtain the profile log-likelihood functions on two parameters. The response plots for different sample sizes provide strong evidence for the estimators' existence and unicity. An application of the power t distribution suggests that the model can be very useful for real data.

• Communications for Statistical Applications and Methods
• /
• 제20권5호
• /
• pp.405-416
• /
• 2013

This paper develops a maximum profile likelihood estimator of unknown parameters of the exponentiated Gumbel distribution based on upper record values. We propose an approximate maximum profile likelihood estimator for a scale parameter. In addition, we derive Bayes estimators of unknown parameters of the exponentiated Gumbel distribution using Lindley's approximation under symmetric and asymmetric loss functions. We assess the validity of the proposed method by using real data and compare these estimators based on estimated risk through a Monte Carlo simulation.

• 한국인터넷방송통신학회논문지
• /
• 제8권3호
• /
• pp.9-14
• /
• 2008

본 논문에서는 Wireless PAN을 위한 MIMO-OFDM시스템에서 근거리 데이터 전송과 더불어 주변장치간 원활한 통신을 위해 채널 sounding 기법을 제안한다. 제안된 기법은 전력 확산 지연을 통해서 임펄스 응답의 지속시간을 파악하며, 감쇠 특성을 갖는 전력 지연 프로 파일(power delay profile) 모델링 하였고, ML(maximum likelihood) 기법으로 채널 응답 계수를 얻었다. Channel sounding 기법을 통해 측정한 크기, 위상, 지연의 변수 값으로 송신단과 수신단간의 채널 전파 특성이 측정 된다. 이 측정된 채널 정보들이 수신단에서 채널 상태를 파악하며, 수신단에서 이 채널 정보를 근거로 통신 링크 성능 향상과 신뢰성을 증대시킬 수 있는 기법을 제안하였다.

• Communications for Statistical Applications and Methods
• /
• 제13권2호
• /
• pp.267-272
• /
• 2006

A procedure for selecting regressors in the linear regression model is suggested using local influence approach. Under an appropriate perturbation scheme, the effect of perturbation of regressors on the profile log-likelihood displacement is assessed for variable selection. A numerical example is provided for illustration.

• 응용통계연구
• /
• 제25권4호
• /
• pp.697-706
• /
• 2012

An Agresti-Coull type test is considered for the difference of binomial proportions in two doubly sampled data subject to common false-positive error. The performance of the test is compared with likelihood-based tests. The Agresti-Coull test has many desirable properties in that it can approximate the nominal significance level well, and has comparable power performance with a computational advantage.

• Journal of the Korean Data and Information Science Society
• /
• 제12권1호
• /
• pp.19-25
• /
• 2001

Random effects models which describe the dependence via random effects in various correlated data have recently received considerable attention in the biomedical literature. They include mixed linear models (MLMs), generatized linear mixed models (GLMMS) and hierarchical generalized linear models (HGLMs). For the inference Lee and Nelder (2000) proposed the first-and second-order REML (restricted maximum likelihood) methods based on hierarchical-likelihood of tee and Welder (1996). In this paper, for Poisson-gamma HGLMs the new methods are theoretically compared with marginal likelihood methods and both methods are illustrated by two practical examples.