• Journal of the Korean Statistical Society
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• v.27 no.3
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• pp.251-264
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• 1998

When the LRT is not feasible, we define quasi-LRT(QLRT) as a modification of the LRT Under some appropriate conditions the QLRT shares Bahadur optimality and Bartlett Adjustability with the LRT. When we can find maximum likelihood estimator under the null parameter space but not under the unrestricted parameter space, our QLRT is Bahadur optimal as is the LRT We suggest the stopping rule of the Newton-Raphson iterations for constructing the QLRT statistics which are Bartlett adjustable.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.109-112
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• 2005

The robust quasi-likelihood (RQL) proposed by Cantoni & Ronchetti (2001) is a robust version of quasi-likelihood. They adopted Huber function to increase the resistance of the RQL estimator to the outliers. They considered the Huber function only of symmetric type. We extend the class of Huber function to include asymmetric types, and derived a method to find the optimal asymmetric one.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.193-198
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• 1999

The estimation of parameters in regression models with multiplicative errors is usually based on the gamma or log-normal likelihoods. Under reciprocal misspecification, we compare the small sample efficiencies of two sets of estimators via a Monte Carlo study. We further consider the case where the errors are a random sample from a Weibull distribution. We compute the asymptotic relative efficiency of quasi-likelihood estimators on the original scale to least squares estimators on the log-transformed scale and perform a Monte Carlo study to compare the small sample performances of quasi-likelihood and least squares estimators.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.755-764
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• 2022

This paper is concerned with power transformations in estimating GARCH volatility. To handle a semi-parametric case for which the exact likelihood is not known, quasi-likelihood (QL) rather than maximum-likelihood method is investigated to best estimate GARCH via maximizing the information criteria. A power transformation is introduced in the innovation generating QL estimating functions and then optimum power is selected by maximizing the profile information. A combination of two different power transformations is also studied in order to increase the parameter estimation efficiency. Nine domestic stock prices data are analyzed to order to illustrate the main idea of the paper. The data span includes Covid-19 pandemic period in which financial time series are really volatile.

• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.367-379
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• 2004

This article deals with the nonparametric analysis of longitudinal data when there exist possible correlations among repeated measurements for a given subject. We consider a quasi-likelihood regression model where a transformation of the regression function through a link function is linear in time-varying coefficients. We investigate the local polynomial approach to estimate the time-varying coefficients, and derive the asymptotic distribution of the estimators in this quasi-likelihood context. A real data set is analyzed as an illustrative example.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.515-520
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• 2002

In general, we use the hierarchical Poisson-gamma model for the Poisson data in generalized linear model. Time effect will be emphasized for the analysis of the observed data to be collected annually for the time period. An extended model with time effect for estimating the effect is proposed. In particularly, we discuss the Quasi likelihood function which is used to numerical approximation for the likelihood function of the parameter.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.133-145
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• 2007

Local linear estimator has a number of advantages over the traditional kernel estimators. The better performance near boundaries is one of them. However, local linear estimator can produce erratic result in sparse regions in the realization of the design and to solve this problem much research has been done. Local linear quasi-likelihood estimator has many common properties with local linear estimator, and it turns out that sparse design can also lead local linear quasi-likelihood estimator to erratic behavior in practice. Several methods to solve this problem are proposed and their finite sample properties are compared by the simulation study.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.541-562
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• 2000

The generalized linear mixed model framework is for handling count-type categorical data as well as for clustered or overdispersed non-Gaussian data, or for non-linear model data. In this study, we review its general formulation and estimation methods, based on quasi-likelihood and Monte-Carlo techniques. The current research areas and topics for further development are also mentioned.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.519-533
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• 2004

In this paper, we suggest quasi-likelihood estimation (QLE) method and its robust version in estimating spatial dependence modelled through variogram used for spatial data modelling. We compare the statistical characteristics of the estimators with other popular least squares estimators of parameters for variogram model by simulation study. The QLE method for estimating spatial dependence has the advantages that it does not need the concept of lags commonly required for least squares estimation methods as well as its statistical superiority. The QLE method also shows the statistical superiority to the other methods for the tested Gaussian and non-Gaussian spatial processes.

• The Korean Journal of Applied Statistics
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• v.4 no.1
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• pp.13-24
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• 1991

This paper presents a Monte Carlo evaluation of estimators for nonlinear consored simultaneous equations models. We examine the performance of the maximum likelihood estimator (MLE), the two-step quasi maximum likelihood estimator (2QMLE) proposed by Lee and Hurd (1989), and another quasi MLe using least squares at the first step (LSAE) under varying degrees of freedom and underlying distributions, Although QMLE's are not necessarily consistent, the Monte Carlo results show that the 2QMLE may be used as an alternative to MLE when MLE is not applicable in practice.