• Title/Summary/Keyword: Statistical distributions

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A Class of Admissible Estimators in the One Parameter Exponential Family

  • Kim, Byung-Hwee
    • Journal of the Korean Statistical Society
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    • v.20 no.1
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    • pp.57-66
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    • 1991
  • This paper deals with the problem of estimating an arbitrary piecewise continuous function of the parameter under squared error loss in the one parameter exponential family. Using Blyth's(1951) method sufficient conditions are given for the admissibility of (possibly generalized Bayes) estimators. Also, some examples are provided for normal, binomial, and gamma distributions.

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Bivariate ROC Curve and Optimal Classification Function

  • Hong, C.S.;Jeong, J.A.
    • Communications for Statistical Applications and Methods
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    • v.19 no.4
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    • pp.629-638
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    • 2012
  • We propose some methods to obtain optimal thresholds and classification functions by using various cutoff criterion based on the bivariate ROC curve that represents bivariate cumulative distribution functions. The false positive rate and false negative rate are calculated with these classification functions for bivariate normal distributions.

Asymptotic Theory for Multi-Dimensional Mode Estimator

  • Kim, Jean-Kyung
    • Journal of the Korean Statistical Society
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    • v.23 no.2
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    • pp.251-269
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    • 1994
  • In this paper we extend Kim and Pollard's cube root asymptotics to other rates of convergence, to establish an asymptotic theory for a multidimensional mode estimator based on uniform kernel with shrinking bandwidths. We obtain rates of convergence depending on shrinking rates of bandwidth and non-normal limit distributions. Optimal decreasing rates of bandwidth are discussed.

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Noninformative Priors for the Intraclass Coefficient of a Symmetric Normal Distribution

  • Chang, In-Hong;Kim, Byung-Hwee
    • Proceedings of the Korean Statistical Society Conference
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    • 2003.10a
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    • pp.15-19
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    • 2003
  • In this paper, we develop the Jeffreys' prior, reference priors and the probability matching priors for the intraclass correlation coefficient of a symmetric normal distribution. We next verify propriety of posterior distributions under those noninformative priors. We examine whether reference priors satisfy the probability matching criterion.

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On the Distribution of the Scaled Residuals under Multivariate Normal Distributions

  • Cheolyong Park
    • Communications for Statistical Applications and Methods
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    • v.5 no.3
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    • pp.591-597
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    • 1998
  • We prove (at least empirically) that some forms of the scaled residuals calculated from i.i.d. multivariate normal random vectors are ancillary. We further show that, if the scaled residuals are ancillary, then they have the same distribution whatever form of rotation is rosed to remove sample correlations.

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Nonparametric Estimation in Regression Model

  • Han, Sang Moon
    • Communications for Statistical Applications and Methods
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    • v.8 no.1
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    • pp.15-27
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    • 2001
  • One proposal is made for constructing nonparametric estimator of slope parameters in a regression model under symmetric error distributions. This estimator is based on the use of idea of Johns for estimating the center of the symmetric distribution together with the idea of regression quantiles and regression trimmed mean. This nonparametric estimator and some other L-estimators are studied by Monte Carlo.

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A Review on Nonparametric Density Estimation Using Wavelet Methods

  • Sungho;Hwa Rak
    • Communications for Statistical Applications and Methods
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    • v.7 no.1
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    • pp.129-140
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    • 2000
  • Wavelets constitute a new orthogonal system which has direct application in density estimation. We introduce a brief wavelet density estimation and summarize some asymptotic results. An application to mixture normal distributions is implemented with S-Plus.

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Robustness of Bayes forecast to Non-normality

  • Bansal, Ashok K.
    • Journal of the Korean Statistical Society
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    • v.7 no.1
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    • pp.11-16
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    • 1978
  • Bayesian procedures are in vogue to revise the parameter estimates of the forecasting model in the light of actual time series data. In this paper, we study the Bayes forecast for demand and the risk when (a) 'noise' and (b) mean demand rate in a constant process model have moderately non-normal probability distributions.

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