• Title, Summary, Keyword: Method of maximum likelihood

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Quasi-Likelihood Approach for Linear Models with Censored Data

  • Ha, Il-Do;Cho, Geon-Ho
    • Journal of the Korean Data and Information Science Society
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    • v.9 no.2
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    • pp.219-225
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    • 1998
  • The parameters in linear models with censored normal responses are usually estimated by the iterative maximum likelihood and least square methods. However, the iterative least square method is simple but hardly has theoretical justification, and the iterative maximum likelihood estimating equations are complicatedly derived. In this paper, we justify these methods via Wedderburn (1974)'s quasi-likelihood approach. This provides an explicit justification for the iterative least square method and also directly the iterative maximum likelihood method for estimating the regression coefficients.

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An Approximation of the Cumulant Generating Functions of Diffusion Models and the Pseudo-likelihood Estimation Method (확산모형에 대한 누율생성함수의 근사와 가우도 추정법)

  • Lee, Yoon-Dong;Lee, Eun-Kyung
    • Journal of the Korean Operations Research and Management Science Society
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    • v.38 no.1
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    • pp.201-216
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    • 2013
  • Diffusion is a basic mathematical tool for modern financial engineering. The theory of the estimation methods for diffusion models is an important topic of the financial engineering. Many researches have been tried to apply the likelihood estimation method for estimating diffusion models. However, the likelihood estimation method for diffusion is complicated and needs much amount of computing. In this paper we develop the estimation methods which are simple enough to be compared to the Euler approximation method, and efficient enough statistically to be compared to the likelihood estimation method. We devise pseudo-likelihood and propose the maximum pseudo-likelihood estimation methods. The pseudo-likelihoods are obtained by approximating the transition density with normal distributions. The means and the variances of the distributions are obtained from the delta expansion suggested by Lee, Song and Lee (2012). We compare the newly suggested estimators with other existing estimators by simulation study. From the simulation study we find the maximum pseudo-likelihood estimator has very similar properties with the maximum likelihood estimator. Also the maximum pseudo-likelihood estimator is easy to apply to general diffusion models, and can be obtained by simple numerical steps.

On the Implementation of Maximum-likelihood Factor Analysis

  • Song, Moon-Sup;Park, Chi-Hoon
    • 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.

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A correction of SE from penalized partial likelihood in frailty models

  • Ha, Il-Do
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.5
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    • pp.895-903
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    • 2009
  • The penalized partial likelihood based on restricted maximum likelihood method has been widely used for the inference of frailty models. However, the standard-error estimate for frailty parameter estimator can be downwardly biased. In this paper we show that such underestimation can be corrected by using hierarchical likelihood. In particular, the hierarchical likelihood gives a statistically efficient procedure for various random-effect models including frailty models. The proposed method is illustrated via a numerical example and simulation study. The simulation results demonstrate that the corrected standard-error estimate largely improves such bias.

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Comparison of parameter estimation methods for normal inverse Gaussian distribution

  • Yoon, Jeongyoen;Kim, Jiyeon;Song, Seongjoo
    • Communications for Statistical Applications and Methods
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    • v.27 no.1
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    • pp.97-108
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    • 2020
  • This paper compares several methods for estimating parameters of normal inverse Gaussian distribution. Ordinary maximum likelihood estimation and the method of moment estimation often do not work properly due to restrictions on parameters. We examine the performance of adjusted estimation methods along with the ordinary maximum likelihood estimation and the method of moment estimation by simulation and real data application. We also see the effect of the initial value in estimation methods. The simulation results show that the ordinary maximum likelihood estimator is significantly affected by the initial value; in addition, the adjusted estimators have smaller root mean square error than ordinary estimators as well as less impact on the initial value. With real datasets, we obtain similar results to what we see in simulation studies. Based on the results of simulation and real data application, we suggest using adjusted maximum likelihood estimates with adjusted method of moment estimates as initial values to estimate the parameters of normal inverse Gaussian distribution.

CONSISTENCY AND ASYMPTOTIC NORMALITY OF A MODIFIED LIKELIHOOD APPROACH CONTINUAL REASSESSMENT METHOD

  • Kang, Seung-Ho
    • Journal of the Korean Statistical Society
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    • v.32 no.1
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    • pp.33-46
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    • 2003
  • The continual reassessment method (CRM) provides a Bayesian estimation of the maximum tolerated dose (MTD) in phase I clinical trials. The CRM has been proposed as an alternative design of the standard design. The CRM has been modified to improve practical feasibility and, recently, the likelihood approach CRM has been proposed. In this paper we investigate the consistency and asymptotic normality of the modified likelihood approach CRM in which the maximum likelihood estimate is used instead of the posterior mean. Small-sample properties of the consistency is examined using complete enumeration. Both the asymptotic results and their small-sample properties show that the modified CRML outperforms the standard design.

Restricted maximum likelihood estimation of a censored random effects panel regression model

  • Lee, Minah;Lee, Seung-Chun
    • Communications for Statistical Applications and Methods
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    • v.26 no.4
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    • pp.371-383
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    • 2019
  • Panel data sets have been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Maximum likelihood (ML) may be the most common statistical method for analyzing panel data models; however, the inference based on the ML estimate will have an inflated Type I error because the ML method tends to give a downwardly biased estimate of variance components when the sample size is small. The under estimation could be severe when data is incomplete. This paper proposes the restricted maximum likelihood (REML) method for a random effects panel data model with a censored dependent variable. Note that the likelihood function of the model is complex in that it includes a multidimensional integral. Many authors proposed to use integral approximation methods for the computation of likelihood function; however, it is well known that integral approximation methods are inadequate for high dimensional integrals in practice. This paper introduces to use the moments of truncated multivariate normal random vector for the calculation of multidimensional integral. In addition, a proper asymptotic standard error of REML estimate is given.

A Comparative Study of the Parameter Estimation Method about the Software Mean Time Between Failure Depending on Makeham Life Distribution (메이크헴 수명분포에 의존한 소프트웨어 평균고장간격시간에 관한 모수 추정법 비교 연구)

  • Kim, Hee Cheul;Moon, Song Chul
    • Journal of Information Technology Applications and Management
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    • v.24 no.1
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    • pp.25-32
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    • 2017
  • For repairable software systems, the Mean Time Between Failure (MTBF) is used as a measure of software system stability. Therefore, the evaluation of software reliability requirements or reliability characteristics can be applied MTBF. In this paper, we want to compare MTBF in terms of parameter estimation using Makeham life distribution. The parameter estimates used the least square method which is regression analyzer method and the maximum likelihood method. As a result, the MTBF using the least square method shows a non-decreased pattern and case of the maximum likelihood method shows a non-increased form as the failure time increases. In comparison with the observed MTBF, MTBF using the maximum likelihood estimation is smallerd about difference of interval than the least square estimation which is regression analyzer method. Thus, In terms of MTBF, the maximum likelihood estimation has efficient than the regression analyzer method. In terms of coefficient of determination, the mean square error and mean error of prediction, the maximum likelihood method can be judged as an efficient method.

On the maximum likelihood estimation for a normal distribution under random censoring

  • Kim, Namhyun
    • Communications for Statistical Applications and Methods
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    • v.25 no.6
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    • pp.647-658
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    • 2018
  • In this paper, we study statistical inferences on the maximum likelihood estimation of a normal distribution when data are randomly censored. Likelihood equations are derived assuming that the censoring distribution does not involve any parameters of interest. The maximum likelihood estimators (MLEs) of the censored normal distribution do not have an explicit form, and it should be solved in an iterative way. We consider a simple method to derive an explicit form of the approximate MLEs with no iterations by expanding the nonlinear parts of the likelihood equations in Taylor series around some suitable points. The points are closely related to Kaplan-Meier estimators. By using the same method, the observed Fisher information is also approximated to obtain asymptotic variances of the estimators. An illustrative example is presented, and a simulation study is conducted to compare the performances of the estimators. In addition to their explicit form, the approximate MLEs are as efficient as the MLEs in terms of variances.

The difference of selectivity of gill net between least square method with polynomials in Kitahara's and maximum likelihood analysis (자망 선택성에서 다항식을 사용한 경우의 Kitahara에 의한 최소제곱법과 최우법의 차이)

  • Park, Hae-Hoon;Millar, Russell B.;Bae, Bong-Seong;An, Heui-Chun;Hwang, Seon-Jae
    • Journal of the Korean Society of Fisheries and Ocean Technology
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    • v.46 no.3
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    • pp.223-231
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    • 2010
  • This paper showed the difference between the selectivity of gill net by least square method with polynomials in Kitahara's and that by maximum likelihood analysis for Japanese sandfish and Korean flounder. Catch experiments for Japanese sandfish using commercial vessels off the eastern coast of Korea were conducted with six different mesh sizes between October and December 2007 and those for Korean flounder with five different mesh sizes between 2008 and 2009. The mesh size of 50% probability of catch corresponding to biological maturity length of fish was not different between that by least square method and that by maximum likelihood analysis for Japanese sandfish, however, a little different for Korean flounder, that is, those mesh sizes of 50% probability of catch for biological maturity length of Korean flounder were 10.6cm and 10.1cm by least square method and maximum likelihood analysis, respectively.