• 응용통계연구
• /
• 제27권2호
• /
• pp.345-355
• /
• 2014

단위 영역의 결점수는 일반적으로 Poisson 분포를 가정한다. 이 Poisson 분포의 확장된 형태로 ZIP(zero-inflated Poisson) 분포를 고려할 수 있는데, 이 모형은 데이터에 0이 많이 관측되는 경우 잘 적합된다고 알려져 있다. 이 논문에서는 ZIP 분포를 따르는 공정을 관리하는 GLR(generalized likelihood ratio) 관리도 절차를 제안하고 있다. 또한 제안된 GLR 관리도의 효율을 기존에 제안된 CUSUM 관리도들과 비교하였다. 그 결과 제안된 GLR 관리도는 모수의 다양한 변화에 대해 효율이 좋거나 또는 효율이 크게 떨어지지 않았고, 특히 CUSUM 관리도에서 모수가 미리 설정한 방향과 다르게 변화했을 때 효율이 크게 나빠지는 문제를 해결할 수 있는 대안이라는 결론을 얻을 수 있었다.

• 응용통계연구
• /
• 제27권5호
• /
• pp.787-796
• /
• 2014

Poisson 분포를 따르는 결점수를 관측하여 공정을 관리할 때 표본 크기를 동일하게 유지하기가 힘든 경우가 많다. 이 논문은 표본 크기가 동일하지 않은 경우 Poisson 공정모수의 변화를 탐지하는 GLR(generalized likelihood ratio) 관리도 절차를 제안하고 있다. 또한 제안된 GLR 관리도의 효율을 모의실험을 통하여 기존에 연구된 CUSUM 관리도들과 비교하였다. 모의실험 결과, 제안된 GLR 관리도는 공정모수의 다양한 변화에 대해 효율이 대체적으로 양호했으며, CUSUM 관리도에서 실제 공정모수의 변화값이 미리 지정한 값과 차이가 많이 날 경우 CUSUM 관리도에 비해 효율이 월등히 좋음을 알 수 있었다.

• 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.

• 대한수학회지
• /
• 제59권1호
• /
• pp.129-150
• /
• 2022

Let L = -∆ + V be a Schrödinger operator, where the potential V belongs to the reverse Hölder class. In this paper, by the subordinative formula, we investigate the generalized Poisson operator P^L_t,σ, 0 < σ < 1, associated with L. We estimate the gradient and the time-fractional derivatives of the kernel of P^L_t,σ, respectively. As an application, we establish a Carleson measure characterization of the Campanato type space 𝒞^𝛄_L (ℝⁿ) via P^L_t,σ.

• Communications for Statistical Applications and Methods
• /
• 제24권4호
• /
• pp.353-365
• /
• 2017

In this article, we proposed a new three-parameter distribution called generalized half-logistic Poisson distribution with a failure rate function that can be increasing, decreasing or upside-down bathtub-shaped depending on its parameters. The new model extends the half-logistic Poisson distribution and has exponentiated half-logistic as its limiting distribution. A comprehensive mathematical and statistical treatment of the new distribution is provided. We provide an explicit expression for the $r^{th}$ moment, moment generating function, Shannon entropy and $R{\acute{e}}nyi$ entropy. The model parameter estimation was conducted via a maximum likelihood method; in addition, the existence and uniqueness of maximum likelihood estimations are analyzed under potential conditions. Finally, an application of the new distribution to a real dataset shows the flexibility and potentiality of the proposed distribution.

• 응용통계연구
• /
• 제23권3호
• /
• pp.585-594
• /
• 2010

본 연구에서는 제로절단된 이변량 일반화 포아송 분포에서 두 반응변수간 산포모수의 효과에 대하여 연구하였다. 모의실험 결과 두 반응변수가 서로 다른 산포를 갖는 경우 이를 무시하는 이변량 포아송 분포나 이변량 음이항 분포에 의한 모형적합은 효율성이 떨어지는 것으로 나타났다. 아울러 본 연구에서는 이와 같은 상이한 산포의 존재유무에 대한 가설검정에서 스코어 검정을 유도하고 우도비 검정과 효율성을 비교하였다.

• Communications for Statistical Applications and Methods
• /
• 제7권3호
• /
• pp.767-772
• /
• 2000

Zellner(1994) introduced the notion of a balanced loss function in the context of a general liner model to reflect both goodness of fit and precision of estimation. We study the perspective of unifying a variety of results both frequentist and Bayesian from Poisson distributions. We show that frequentist and Bayesian results for balanced loss follow from and also imply related results for quadratic loss functions reflecting only precision of estimation. Several examples are given for Poisson distribution.

• Communications for Statistical Applications and Methods
• /
• 제16권6호
• /
• pp.971-978
• /
• 2009

Poisson generalized linear mixed models(GLMMs) have been widely used for the analysis of clustered or correlated count data. For the inference marginal likelihood, which is obtained by integrating out random effects is often used. It gives maximum likelihood(ML) estimator, but the integration is usually intractable. In this paper, we propose how to obtain the ML estimator via Laplace approximation based on hierarchical-likelihood (h-likelihood) approach under the Poisson GLMMs. In particular, the h-likelihood avoids the integration itself and gives a statistically efficient procedure for various random-effect models including GLMMs. The proposed method is illustrated using two practical examples and simulation studies.

• Journal of the Korean Data and Information Science Society
• /
• 제19권4호
• /
• pp.1353-1360
• /
• 2008

An estimating procedure is introduced for the nonlinear mixed-effect Poisson regression, for longitudinal study, where data from different subjects are independent whereas data from same subject are correlated. The proposed procedure provides the estimates of the mean function of the response variables, where the canonical parameter is related to the input vector in a nonlinear form. The generalized cross validation function is introduced to choose optimal hyper-parameters in the procedure. Experimental results are then presented, which indicate the performance of the proposed estimating procedure.

• Journal of the Korean Data and Information Science Society
• /
• 제28권4호
• /
• pp.927-936
• /
• 2017

To analyze longitudinal count data, Poisson linear mixed models are commonly used. In the models the random effects covariance matrix explains both within-subject variation and serial correlation of repeated count outcomes. When the random effects covariance matrix is assumed to be misspecified, the estimates of covariates effects can be biased. Therefore, we propose reasonable and flexible structures of the covariance matrix using autoregressive and moving average Cholesky decomposition (ARMACD). The ARMACD factors the covariance matrix into generalized autoregressive parameters (GARPs), generalized moving average parameters (GMAPs) and innovation variances (IVs). Positive IVs guarantee the positive-definiteness of the covariance matrix. In this paper, we use the ARMACD to model the random effects covariance matrix in Poisson loglinear mixed models. We analyze epileptic seizure data using our proposed model.