• Title/Summary/Keyword: Statistical modeling

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SOME GENERALIZED GAMMA DISTRIBUTION

  • Nadarajah Saralees;Gupta Arjun K.
    • Journal of the Korean Statistical Society
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    • v.36 no.1
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    • pp.93-109
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    • 2007
  • Gamma distributions are some of the most popular models for hydrological processes. In this paper, a very flexible family which contains the gamma distribution as a particular case is introduced. Evidence of flexibility is shown by examining the shape of its pdf and the associated hazard rate function. A comprehensive treatment of the mathematical properties is provided by deriving expressions for the nth moment, moment generating function, characteristic function, Renyi entropy and the asymptotic distribution of the extreme order statistics. Estimation and simulation issues are also considered. Finally, a detailed application to drought data from the State of Nebraska is illustrated.

Bayesian Conjugate Analysis for Transition Probabilities of Non-Homogeneous Markov Chain: A Survey

  • Sung, Minje
    • Communications for Statistical Applications and Methods
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    • v.21 no.2
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    • pp.135-145
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    • 2014
  • The present study surveys Bayesian modeling structure for inferences about transition probabilities of Markov chain. The motivation of the study came from the data that shows transitional behaviors of emotionally disturbed children undergoing residential treatment program. Dirichlet distribution was used as prior for the multinomial distribution. The analysis with real data was implemented in WinBUGS programming environment. The performance of the model was compared to that of alternative approaches.

BINARY RANDOM POWER APPROACH TO MODELING ASYMMETRIC CONDITIONAL HETEROSCEDASTICITY

  • KIM S.;HWANG S.Y.
    • Journal of the Korean Statistical Society
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    • v.34 no.1
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    • pp.61-71
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    • 2005
  • A class of asymmetric ARCH processes is proposed via binary random power transformations. This class accommodates traditional nonlinear models such as threshold ARCH (Rabemanjara and Zacoian (1993)) and Box-Cox type ARCH models(Higgins and Bera (1992)). Stationarity condition of the model is addressed. Iterative least squares(ILS) and pseudo maximum like-lihood(PML) methods are discussed for estimating parameters and related algorithms are presented. Illustrative analysis for Korea Stock Prices Index (KOSPI) data is conducted.

Multiprocess Dynamic Poisson Mode1s: The Covariates Case

  • Shim, Joo-Yong;Sohn, Joong-Kweon
    • Journal of the Korean Statistical Society
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    • v.27 no.3
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    • pp.279-288
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    • 1998
  • We propose a multiprocess dynamic Poisson model for the analysis of Poisson process with the covariates. The algorithm for the recursive estimation of the parameter vector modeling time-varying effects of covariates is suggested. Also the algorithm for forecasting of numbers of events at the next time point based on the information gathered until the current time is suggested.

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Bayesian Analysis for a Functional Regression Model with Truncated Errors in Variables

  • Kim, Hea-Jung
    • Journal of the Korean Statistical Society
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    • v.31 no.1
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    • pp.77-91
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    • 2002
  • This paper considers a functional regression model with truncated errors in explanatory variables. We show that the ordinary least squares (OLS) estimators produce bias in regression parameter estimates under misspecified models with ignored errors in the explanatory variable measurements, and then propose methods for analyzing the functional model. Fully parametric frequentist approaches for analyzing the model are intractable and thus Bayesian methods are pursued using a Markov chain Monte Carlo (MCMC) sampling based approach. Necessary theories involved in modeling and computation are provided. Finally, a simulation study is given to illustrate and examine the proposed methods.

Bayesian Estimation of State-Space Model Using the Hybrid Monte Carlo within Gibbs Sampler

  • Park, Ilsu
    • Communications for Statistical Applications and Methods
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    • v.10 no.1
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    • pp.203-210
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    • 2003
  • In a standard Metropolis-type Monte Carlo simulation, the proposal distribution cannot be easily adapted to "local dynamics" of the target distribution. To overcome some of these difficulties, Duane et al. (1987) introduced the method of hybrid Monte Carlo(HMC) which combines the basic idea of molecular dynamics and the Metropolis acceptance-rejection rule to produce Monte Carlo samples from a given target distribution. In this paper, using the HMC within Gibbs sampler, an asymptotical estimate of the smoothing mean and a general solution to state space modeling in Bayesian framework is obtaineds obtained.

Asymmetric Modeling in Beta-ARCH Processes

  • S. Y. Hwang;Kahng, Myung-Wook
    • Journal of the Korean Statistical Society
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    • v.31 no.4
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    • pp.459-468
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    • 2002
  • A class of asymmetric beta-ARCH processes is proposed and connections to traditional ARCH models are explained. Geometric ergodicity of the model is discussed. Conditional least squares as well as maximum likelihood estimators of parameters and their limit results are also presented. A test for symmetry of the model is studied with limiting power of test statistic given.

A SKEWED GENERALIZED t DISTRIBUTION

  • NADARAJAH SARALEES
    • Journal of the Korean Statistical Society
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    • v.34 no.4
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    • pp.311-329
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    • 2005
  • Skewed t distributions have attracted significant attention in the last few years. In this paper, a generalization - referred to as the skewed generalized t distribution - with the pdf f(x) = 2g(x)G(${\lambda}x$) is introduced, where g(${\cdot}$) and G (${\cdot}$) are taken, respectively, to be the pdf and the cdf of the generalized t distribution due to McDonald and Newey (1984, 1988). Several particular cases of this distribution are identified and various representations for its moments derived. An application is provided to rainfall data from Orlando, Florida.

Multilevel Mediation Analysis: Statistical Methods, Analytic Procedure, and a Real Example (다층자료의 매개효과 분석: 통계방법, 분석절차 및 실례)

  • Park, Sun-Mi;Bak, Byung-Gee
    • Science of Emotion and Sensibility
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    • v.19 no.4
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    • pp.95-110
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    • 2016
  • The purpose of this study was to propose a proper method for the multilevel mediation analysis, for which the hierarchical method should be utilized, then MLM (multilevel modeling) approach as a hierarchical method has been popularly utilized until MSEM (multilevel structural equation modeling) approach was not proposed. This purpose was covered by three research questions about statistical methods, analytic procedure, and real example. First, MSEM statistical method was preferred to MLM method for its estimation accuracy and analytic flexibility. Second, the four-step procedures of model building, assumption examination, model comparison, and coefficient testing were proposed for the multilevel mediation analysis. Third, the real data of 2695 students of elementary and secondary schools and 89 teachers were analyzed in the multilevel directions of $2{\rightarrow}2{\rightarrow}1$ and $1{\rightarrow}1{\rightarrow}2$. Out of these directions of $2{\rightarrow}2{\rightarrow}1$, and $1{\rightarrow}1{\rightarrow}2$ model, only the coefficient of $2{\rightarrow}2{\rightarrow}1$ model was significant at the 95% CI. Mplus programs used for the real example are attached on the Appendix. Based on the results, significance and limitations of this study, were discussed in detail.

Statistical Timing Analysis of Partially-Depleted SOI Gates (부분 공핍형 SOI 게이트의 통계적 타이밍 분석)

  • Kim, Kyung-Ki
    • Journal of the Institute of Electronics Engineers of Korea SD
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    • v.44 no.12
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    • pp.31-36
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    • 2007
  • This paper presents a novel statistical characterization for accurate timing analysis in Partially-Depleted Silicon-On-Insulator (PD-SOI) circuits in BSIMSOI3.2 100nm technology. The proposed timing estimate algorithm is implemented in Matlab, Hspice, and C, and it is applied to ISCAS85 benchmarks. The results show that the error is within 5% compared with Monte Carlo simulation results.