• 제목/요약/키워드: Maximum likelihood estimation

검색결과 979건 처리시간 0.035초

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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    • 제26권4호
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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.

Approximate Maximum Likelihood Estimation for the Three-Parameter Weibull Distribution

  • Kang, S.B.;Cho, Y.S.;Choi, S.H.
    • Communications for Statistical Applications and Methods
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    • 제8권1호
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    • pp.209-217
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    • 2001
  • We obtain the approximate maximum likelihood estimators (AMLEs) for the scale and location parameters $\theta$ and $\mu$ in the three-parameter Weibull distribution based on Type-II censored samples. We also compare the AMLEs with the modified maximum likelihood estimators (MMLEs) in the sense of the mean squared error (MSE) based on complete sample.

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임의 중단모형에서 최소제곱법을 이용한 와이블분포의 모수 추정 (An Estimation of Parameters in Weibull Distribution Using Least Squares Method under Random Censoring Model)

  • 이우동
    • Journal of the Korean Data and Information Science Society
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    • 제7권2호
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    • pp.263-272
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    • 1996
  • 임의의 기계에 대한 수명의 분포는 와이블분포를 하는 경우가 흔하다. 그리고 현실적으로 기계의 수명시간을 검정할 때, 시험시간및 여러 환경적인 제약에 의하여 표본으로 주어진 기계의 수명을 모두 관측하기는 어렵다. 그래서, 본 연구에서는 임의 중단모형 하에서 와이블분포의 모수를 최소제곱법(least squares method)을 이용하여 추정하고 기존의 최대우도추정량(maximum likelihood estimates)과 효율성의 측면에서 비교하고자 한다.

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Maximum penalized likelihood estimation for a stress-strength reliability model using complete and incomplete data

  • Hassan, Marwa Khalil
    • Communications for Statistical Applications and Methods
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    • 제25권4호
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    • pp.355-371
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    • 2018
  • The two parameter negative exponential distribution has many practical applications in queuing theory such as the service times of agents in system, the time it takes before your next telephone call, the time until a radioactive practical decays, the distance between mutations on a DNA strand, and the extreme values of annual snowfall or rainfall; consequently, has many applications in reliability systems. This paper considers an estimation problem of stress-strength model with two parameter negative parameter exponential distribution. We introduce a maximum penalized likelihood method, Bayes estimator using Lindley approximation to estimate stress-strength model and compare the proposed estimators with regular maximum likelihood estimator for complete data. We also introduce a maximum penalized likelihood method, Bayes estimator using a Markov chain Mote Carlo technique for incomplete data. A Monte Carlo simulation study is performed to compare stress-strength model estimates. Real data is used as a practical application of the proposed model.

A Note on a New Two-Parameter Lifetime Distribution with Bathtub-Shaped Failure Rate Function

  • Wang, F.K.
    • International Journal of Reliability and Applications
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    • 제3권1호
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    • pp.51-60
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    • 2002
  • This paper presents the methodology for obtaining point and interval estimating of the parameters of a new two-parameter distribution with multiple-censored and singly censored data (Type-I censoring or Type-II censoring) as well as complete data, using the maximum likelihood method. The basis is the likelihood expression for multiple-censored data. Furthermore, this model can be extended to a three-parameter distribution that is added a scale parameter. Then, the parameter estimation can be obtained by the graphical estimation on probability plot.

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Comparison of Nonparametric Maximum Likelihood and Bayes Estimators of the Survival Function Based on Current Status Data

  • Kim, Hee-Jeong;Kim, Yong-Dai;Son, Young-Sook
    • Communications for Statistical Applications and Methods
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    • 제14권1호
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    • pp.111-119
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    • 2007
  • In this paper, we develop a nonparametric Bayesian methodology of estimating an unknown distribution function F at the given survival time with current status data under the assumption of Dirichlet process prior on F. We compare our algorithm with the nonparametric maximum likelihood estimator through application to simulated data and real data.

A COMPARATIVE EVALUATION OF THE ESTIMATORS OF THE 2-PARAMETER GENERALIZED PARETO DISTRIBUTION

  • Singh, V.P.;Ahmad, M.;Sherif, M.M.
    • Water Engineering Research
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    • 제4권3호
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    • pp.155-173
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    • 2003
  • Parameters and quantiles of the 2-parameter generalized Pareto distribution were estimated using the methods of regular moments, modified moments, probability weighted moments, linear moments, maximum likelihood, and entropy for Monte Carlo-generated samples. The performance of these seven estimators was statistically compared, with the objective of identifying the most robust estimator. It was found that in general the methods of probability-weighted moments and L-moments performed better than the methods of maximum likelihood estimation, moments and entropy, especially for smaller values of the coefficient of variation and probability of exceedance.

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Parameter Estimation for an Infinite Dimensional Stochastic Differential Equation

  • Kim, Yoon-Tae
    • Journal of the Korean Statistical Society
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    • 제25권2호
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    • pp.161-173
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    • 1996
  • When we deal with a Hilbert space-valued Stochastic Differential Equation (SDE) (or Stochastic Partial Differential Equation (SPDE)), depending on some unknown parameters, the solution usually has a Fourier series expansion. In this situation we consider the maximum likelihood method for the statistical estimation problem and derive the asymptotic properties (consistency and normality) of the Maximum Likelihood Estimator (MLE).

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On Asymptotic Properties of a Maximum Likelihood Estimator of Stochastically Ordered Distribution Function

  • Oh, Myongsik
    • Communications for Statistical Applications and Methods
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    • 제20권3호
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    • pp.185-191
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    • 2013
  • Kiefer (1961) studied asymptotic behavior of empirical distribution using the law of the iterated logarithm. Robertson and Wright (1974a) discussed whether this type of result would hold for a maximum likelihood estimator of a stochastically ordered distribution function; however, we show that this cannot be achieved. We provide only a partial answer to this problem. The result is applicable to both estimation and testing problems under the restriction of stochastic ordering.