• 제목/요약/키워드: CSAM

검색결과 554건 처리시간 0.018초

A Simultaneous Test Procedure

  • Hong, Seungman;Cho, Joong-Jae;Park, Hyo-Il
    • Communications for Statistical Applications and Methods
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    • 제21권1호
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    • pp.11-22
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    • 2014
  • In this study, we propose a simultaneous test procedure based on the individual - values for each sub-null hypothesis with several well-known combining functions. We then compare the efficiency of our procedure with existing tests by obtaining empirical powers through a simulation study. Finally, we discuss some interesting features related to simultaneous test and point out a misconduct for the simulation study published in the previous work.

Logistic Regression Classification by Principal Component Selection

  • Kim, Kiho;Lee, Seokho
    • Communications for Statistical Applications and Methods
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    • 제21권1호
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    • pp.61-68
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    • 2014
  • We propose binary classification methods by modifying logistic regression classification. We use variable selection procedures instead of original variables to select the principal components. We describe the resulting classifiers and discuss their properties. The performance of our proposals are illustrated numerically and compared with other existing classification methods using synthetic and real datasets.

A Note on the Characteristic Function of Multivariate t Distribution

  • Song, Dae-Kun;Park, Hyoung-Jin;Kim, Hyoung-Moon
    • Communications for Statistical Applications and Methods
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    • 제21권1호
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    • pp.81-91
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    • 2014
  • This study derives the characteristic functions of (multivariate/generalized) t distributions without contour integration. We extended Hursts method (1995) to (multivariate/generalized) t distributions based on the principle of randomization and mixtures. The derivation methods are relatively straightforward and are appropriate for graduate level statistics theory courses.

Nonparametric Estimation of Distribution Function using Bezier Curve

  • Bae, Whasoo;Kim, Ryeongah;Kim, Choongrak
    • Communications for Statistical Applications and Methods
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    • 제21권1호
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    • pp.105-114
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    • 2014
  • In this paper we suggest an efficient method to estimate the distribution function using the Bezier curve, and compare it with existing methods by simulation studies. In addition, we suggest a robust version of cross-validation criterion to estimate the number of Bezier points, and showed that the proposed method is better than the existing methods based on simulation studies.

Nonresponse Adjusted Raking Ratio Estimation

  • Park, Mingue
    • Communications for Statistical Applications and Methods
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    • 제22권6호
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    • pp.655-664
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    • 2015
  • A nonresponse adjusted raking ratio estimator that consists of weighting adjustment using estimated response probability and raking procedure is often used to reduce the nonresponse bias and keep the calibration property of the estimator. We investigated asymptotic properties of nonresponse adjusted raking ratio estimator and proposed a variance estimator. A simulation study is used to examine the performance of suggested estimators.

Gibbs Sampling for Double Seasonal Autoregressive Models

  • Amin, Ayman A.;Ismail, Mohamed A.
    • Communications for Statistical Applications and Methods
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    • 제22권6호
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    • pp.557-573
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    • 2015
  • In this paper we develop a Bayesian inference for a multiplicative double seasonal autoregressive (DSAR) model by implementing a fast, easy and accurate Gibbs sampling algorithm. We apply the Gibbs sampling to approximate empirically the marginal posterior distributions after showing that the conditional posterior distribution of the model parameters and the variance are multivariate normal and inverse gamma, respectively. The proposed Bayesian methodology is illustrated using simulated examples and real-world time series data.

Bayesian Pattern Mixture Model for Longitudinal Binary Data with Nonignorable Missingness

  • Kyoung, Yujung;Lee, Keunbaik
    • Communications for Statistical Applications and Methods
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    • 제22권6호
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    • pp.589-598
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    • 2015
  • In longitudinal studies missing data are common and require a complicated analysis. There are two popular modeling frameworks, pattern mixture model (PMM) and selection models (SM) to analyze the missing data. We focus on the PMM and we also propose Bayesian pattern mixture models using generalized linear mixed models (GLMMs) for longitudinal binary data. Sensitivity analysis is used under the missing not at random assumption.

Case Deletion Diagnostics for Intraclass Correlation Model

  • Kim, Myung Geun
    • Communications for Statistical Applications and Methods
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    • 제21권3호
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    • pp.253-260
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    • 2014
  • The intraclass correlation model has a long history of applications in several fields of research. Case deletion diagnostic methods for the intraclass correlation model are proposed. Based on the likelihood equations, we derive a formula for a case deletion diagnostic method which enables us to investigate the influence of observations on the maximum likelihood estimates of the model parameters. Using the Taylor series expansion we develop an approximation to the likelihood distance. Numerical examples are provided for illustration.

On Bounds for Moments of Unimodal Distributions

  • Sharma, R.;Bhandaria, R.
    • Communications for Statistical Applications and Methods
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    • 제21권3호
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    • pp.201-212
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    • 2014
  • We provide a simple basic method to find bounds for higher order moments of unimodal distributions in terms of lower order moments when the random variable takes value in a given finite real interval. The bounds for moments in terms of the geometric mean of the distribution are also derived. Both continuous and discrete cases are considered. The bounds for the ratio and difference of moments are obtained. The special cases provide refinements of several well-known inequalities, such as Kantorovich inequality and Krasnosel'skii and Krein inequality.

A Note on the History of the Gambler's Ruin Problem

  • Song, Seongjoo;Song, Jongwoo
    • Communications for Statistical Applications and Methods
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    • 제20권2호
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    • pp.157-168
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    • 2013
  • This paper deals with the history of one of the well-known and historically important problems in probability, "Gambler's ruin". This problem was first solved by Pascal and Fermat and published by Huygens in 1657. It was studied and extended by many probabilists in early years and thus, it became an important problem in probability history, introducing many new concepts. We would like to introduce the problem in detail to readers and share the ideas on how new problems are developed, relating to old problems.