• Title/Summary/Keyword: Random sample

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Heteroscedasticity of Random Effects in Crossover Design

  • Ahn, Chul-H.
    • Proceedings of the Korean Statistical Society Conference
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    • 2002.11a
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    • pp.79-83
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    • 2002
  • A phase III clinical trial of a new drug for neutropenia induced by chemotherapy is presented and consider adding random effects in crossover design which was used in the clinical study. The diagnostics for its heteroscedasticity based on score statistic is derived for detecting homoscedasticity of errors in crossover design. A small simulation study is peformed to investigate the finite sample behaviour of the test statistic which is known to have an asymptotic chi-square distribution under the null hypothesis.

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STRONG LAWS FOR WEIGHTED SUMS OF I.I.D. RANDOM VARIABLES

  • Cai, Guang-Hui
    • Communications of the Korean Mathematical Society
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    • v.21 no.4
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    • pp.771-778
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    • 2006
  • Strong laws are established for linear statistics that are weighted sums of a random sample. We show extensions of the Marcinkiewicz-Zygmund strong laws under certain moment conditions on both the weights and the distribution. The result obtained extends and sharpens the result of Sung ([12]).

Tail Probability Approximations for the Ratio of the Independent Random Variables

  • Cho, Dae-Hyeon
    • Journal of the Korean Data and Information Science Society
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    • v.7 no.2
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    • pp.189-201
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    • 1996
  • In this paper, we study the saddlepoint approximations for the ratio of independent random variables. In Section 2, we derive the saddlepoint approximation to the density. And in Section 3, we derive two approximation formulae for the tail probability, one by following Daniels'(1987) method and the other by following Lugannani and Rice's (1980). In Section 4, we represent some numerical examples which show that the errors are small even for small sample size.

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Nonparametric Estimation of Pr[X>Y] from Random Censored Data (임의절단 자료에서의 Pr[X>Y]의 비모수적 추정)

  • Jeong, Hai-Sung;Kim, Jae-Joo
    • Journal of Korean Society for Quality Management
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    • v.23 no.2
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    • pp.91-102
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    • 1995
  • For two independent random variables X and Y, the functional R=Pr[X>Y] is of practical importance in reliability. X can be interpreted as the strength of a component subjected to a stress Y, and R is the component's reliability. In this paper nonparametric approach to estimation of R based on censored observations in the strength variables is analyzed and compared by simulations in the moderate sample sizes.

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A Note on Admissibility and Finite Admissibility in Estimation

  • Byung Hwee Kim;Tae Ryoung Park
    • Communications for Statistical Applications and Methods
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    • v.1 no.1
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    • pp.87-93
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    • 1994
  • Consider the problem of estimating the parameter of the model in which an observable random variable is represented by a unknown scalar parameter plus another random variable and the parameter, sample, and decision spaces consist of all integers. We first characterize the class of all admissible estimators and then characterize the class of all finitely admissible estimators. Finally, we show that two classes are identical.

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Rank Tests for Multivariate Linear Models in the Presence of Missing Data

  • Lee, Jae-Won;David M. Reboussin
    • Journal of the Korean Statistical Society
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    • v.26 no.3
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    • pp.319-332
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    • 1997
  • The application of multivariate linear rank statistics to data with item nonresponse is considered. Only a modest extension of the complete data techniques is required when the missing data may be thought of as a random sample, and an appropriate modification of the covariances is derived. A proof of the asymptotic multivariate normality is given. A review of some related results in the literature is presented and applications including longitudinal and repeated measures designs are discussed.

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Nonparametric Stock Price Prediction (비모수 주가예측 모형)

  • Choi, Sung-Sup;Park, Joo-Hean
    • The Korean Journal of Financial Management
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    • v.12 no.2
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    • pp.221-237
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    • 1995
  • When we apply parametric models to the movement of stock prices, we don't know whether they are really correct specifications. In the paper, any prior conditional mean structure is not assumed. By applying the nonparametric model, we see if it better performs (than the random walk model) in terms of out-of-sample prediction. An interesting finding is that the random walk model is still the best. There doesn't seem to exist any form of nonlinearity (not to mention linearity) in stock prices that can be exploitable in terms of point prediction.

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BOOTSTRAPPING GENERALIZED LINEAR MODELS WITH RANDOM REGRESSORS

  • Lee, Kee-Won;Kim, Choong-Rak;Sohn, Keon-Tae;Jeong, Kwang-Mo
    • Journal of the Korean Statistical Society
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    • v.21 no.1
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    • pp.70-79
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    • 1992
  • The generalized linear models with random regrssors case are studied for bootstrapping. Only the natural link functions are considered. It is shown that the bootstrap approximation to the distribution of the maximum likelihood estimators is valid for almost all sample sequences. A slight extension of this model is also considered.

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Extraction of Corresponding Points Using EMSAC Algorithm (EMSAC을 이용한 대응점 추출 알고리즘에 관한 연구)

  • Wie, Eun-Young;Ye, Soo-Young;Joo, Jae-Hum;Nam, Ki-Gon
    • Proceedings of the IEEK Conference
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    • 2006.06a
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    • pp.405-406
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    • 2006
  • This paper proposes the new algorithm for the extraction of the corresponding points. Our algorithm is based on RANSAC(Random Sample Consensus) with EM(Expectation-Maximization). In the procedure of RANSAC, N-points are selected by the result of EM instead of the random selection. EM+SAC algorithm is applied to the correspondence for the mosaicing.

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Tail Probability Approximations for the Ratio of two Independent Sequences of Random Variables

  • Cho, Dae-Hyeon
    • Journal of the Korean Data and Information Science Society
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    • v.10 no.2
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    • pp.415-428
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    • 1999
  • In this paper, we study the saddlepoint approximations for the ratio of two independent sequences of random variables. In Section 2, we review the saddlepoint approximation to the probability density function. In section 3, we derive an saddlepoint approximation formular for the tail probability by following Daniels'(1987) method. In Section 4, we represent a numerical example which shows that the errors are small even for small sample size.

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