• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.23-32
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• 2007

We consider penalized likelihood regression with data from the negative binomial distribution with unknown shape parameter. Smoothing parameter selection and asymptotically efficient low dimensional approximations are employed for negative binomial data along with shape parameter estimation through several different algorithms.

• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.559-570
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• 1998

This paper introduces doubly Winsorized Poisson auto-model by truncating the support of a Poisson random variable both from above and below, and shows that this model has a same form of negpotential function as regular Poisson auto-model and one-way Winsorized Poisson auto-model. Strategies for maximum likelihood estimation of parameters are discussed. In addition to exact maximum likelihood estimation, Monte Carlo maximum likelihood estimation may be applied to this model.

• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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• pp.651-656
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• 2005

In this paper the quasi-likelihood function was proposed and the estimators which are the solutions of the estimating equations for estimation of a class of nonlinear time series models. We compare the performances of the proposed estimators with those of the ML estimators under the heavy-railed distributions by simulation.

• Journal of the Korean Statistical Society
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• v.30 no.1
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• pp.77-87
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• 2001

Some asymptotic results on the local likelihood density estimator of Copas(1995) are derived when the locally parametric model has several parameters. It turns out that it has the same asymptotic mean squared error as that of Hjort and Jones(1996).

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.983-988
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• 2003

When the available sample is multiply Type-II censored, the maximum likelihood estimators of the location and the scale parameters of two-parameter exponential distribution do not admit explicitly. In this case, we propose some approximate maximum likelihood estimators by approximating the likelihood equations appropriately. We present an example to illustrate these estimation methods.

• Journal of applied mathematics & informatics
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• v.26 no.5_6
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• pp.1133-1138
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• 2008

A general method of detecting outliers based on a change of likelihood by using the influence function is suggested. It can be applied to all kinds of distributions that are specified by parameters. For the multivariate normal case, specific computations are made to get the corresponding conditional influence function. A numerical example is provided for illustration.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.269-276
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• 2000

there are several methods available for estimating parameters in overdispersed binary response data with the litter effect. Simulations are performed to compare methods for estimating an overall mean and an overdispersion parameter using moments a maximum likelihood under a beta-binomial distribution a maximum quasi-likelihood and a maximum extended quasi-likelihood.

• Journal of the Korean Statistical Society
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• v.9 no.1
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• pp.13-29
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• 1980

The statistical theory of factor analysis is briefly reviewed with emphasis on the maximum-likelihood method. A modified version of Joreskog(1975) is used for the implementation of the maximum-likelihood method. For the minimization of the conditional minimum function, an adaptive Newton-Raphson method is applied.

• Journal of the Korean Statistical Society
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• v.27 no.2
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• pp.221-235
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• 1998

Suppose that an observed process can be written as the additive model of the signal process and the noise process with unknown parameters. In practice the signal process is not directly observed. We consider the problem of estimating parameter from the observation process using the quasi-likelihood method.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.169-176
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• 2014

Counting processes are widely used in many fields, whose properties are determined by the intensity function. For estimation of the parameters of the intensity functions when the process is observed continuously over a fixed interval, the likelihood function is of interest. However in the literature there are only heuristic derivations and some results are not coincident. We thus in this note derive the likelihood function of the counting process in a rigorous way. So this note fill up a hole in derivation of the likelihood function.