• Title/Summary/Keyword: L-Statistics

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Kullback-Leibler Information in View of an Extended Version of κ-Records

  • Ahmadi, Mosayeba;Mohtashami Borzadaran, G.R.
    • Communications for Statistical Applications and Methods
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    • v.20 no.1
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    • pp.1-13
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    • 2013
  • This paper introduces an extended version of ${\kappa}$-records. Kullback-Leibler (K-L) information between two generalized distributions arising from ${\kappa}$-records is derived; subsequently, it is shown that K-L information does not depend on the baseline distribution. The behavior of K-L information for order statistics and ${\kappa}$-records, is studied. The exact expressions for K-L information between distributions of order statistics and upper (lower) ${\kappa}$-records are obtained and some special cases are provided.

Some Subcategories of The Category IRe$l_{R}$(H) (범주 IRe $l_{R}$(H)의 부분범주)

  • K. Hur;H. W. Kang;J. H. Ryou;H. K. Song
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2003.05a
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    • pp.29-32
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    • 2003
  • We introduce the subcategories IRe $l_{PR}$ (H), IRe $l_{PO}$ (H) and IRe $l_{E}$(H) of IRe $l_{R}$(H) and study their structures in a viewpoint of the topological universe introduced by L.D.Nel. In particular, the category IRe $l_{R}$(H)(resp. IRe $l_{P}$(H) and IRe $l_{E}$(H)) is a topological universe eve, Set. Moreover, we show that IRe $l_{E}$(H) has exponential objects.ial objects.

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Comparison Studies of Classification Methods based on L1-Distance and L1-Data Depth (L1-거리와 L1-데이터뎁스를 이용한 분류방법의 비교연구)

  • Baek Soo-Jin;Hwang Jin-Soo;Kim Jean-Kyung
    • The Korean Journal of Applied Statistics
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    • v.19 no.1
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    • pp.183-193
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    • 2006
  • We consider a new classification method(DnDclass) combining two classification rules based on $L_1$-distance(L1DISTclass) and $L_1$-data depth(L1DDclass). To investigate characteristics and to evaluate the performance of these classification methods, we use simulation data in various settings. Through this simulation study, we can confirm that the new method, DnDclass, performs relatively well in many cases.

Projected Circular and l-Axial Skew-Normal Distributions

  • Seo, Han-Son;Shin, Jong-Kyun;Kim, Hyoung-Moon
    • The Korean Journal of Applied Statistics
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    • v.22 no.4
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    • pp.879-891
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    • 2009
  • We developed the projected l-axial skew-normal(LASN) family of distributions for I-axial data. The LASN family of distributions contains the semicircular skew-normal(SCSN) and the circular skew-normal(CSN) families of distributions as special cases. The LASN densities are similar to the wrapped skew-normal densities for the small values of the scale parameter. However CSN densities have more heavy tails than those of the wrapped skew-normal densities on the circle. Furthermore the CSN densities have two modes as the scale parameter increases. The LASN distribution has very convenient mathematical features. We extend the LASN family of distributions to a bivariate case.

Random Elements in $L^1(R)$ and Kernel Density Estimators

  • Lee, Sung-Ho;Lee, Robert -Taylor
    • Journal of the Korean Statistical Society
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    • v.22 no.1
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    • pp.83-91
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    • 1993
  • Random elements in $L^1(R)$ and some properties of $L^1(R)$ space are investigated with application to kernel density estimators. A weak law of large numbers for compact uniformly integrable random elements is introduced for further application.

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REGULARITY OF THE GENERALIZED POISSON OPERATOR

  • Li, Pengtao;Wang, Zhiyong;Zhao, Kai
    • Journal of the Korean Mathematical Society
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    • v.59 no.1
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    • pp.129-150
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    • 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 PLt,σ, 0 < σ < 1, associated with L. We estimate the gradient and the time-fractional derivatives of the kernel of PLt,σ, respectively. As an application, we establish a Carleson measure characterization of the Campanato type space 𝒞𝛄L (ℝn) via PLt,σ.

Adaptive M-estimation in Regression Model

  • Han, Sang-Moon
    • Communications for Statistical Applications and Methods
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    • v.10 no.3
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    • pp.859-871
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    • 2003
  • In this paper we introduce some adaptive M-estimators using selector statistics to estimate the slope of regression model under the symmetric and continuous underlying error distributions. This selector statistics is based on the residuals after the preliminary fit L$_1$ (least absolute estimator) and the idea of Hogg(1983) and Hogg et. al. (1988) who used averages of some order statistics to discriminate underlying symmetric distributions in the location model. If we use L$_1$ as a preliminary fit to get residuals, we find the asymptotic distribution of sample quantiles of residual are slightly different from that of sample quantiles in the location model. If we use the functions of sample quantiles of residuals as selector statistics, we find the suitable quantile points of residual based on maximizing the asymptotic distance index to discriminate distributions under consideration. In Monte Carlo study, this adaptive M-estimation method using selector statistics works pretty good in wide range of underlying error distributions.

l-STEP GENERALIZED COMPOSITE ESTIMATOR UNDER 3-WAY BALANCED ROTATION DESIGN

  • KIM K. W.;PARK Y. S.;KIM N. Y.
    • Journal of the Korean Statistical Society
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    • v.34 no.3
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    • pp.219-233
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    • 2005
  • The 3-way balanced multi-level rotation design has been discussed (Park Kim and Kim, 2003), where the 3-way balancing is done on interview time, in monthly sample and rotation group and recall time. A greater advantage of 3-way balanced design is accomplished by an estimator. To obtain the advantage, we generalized previous generalized composite estimator (GCE). We call this as l-step GCE. The variance of the l-step GCE's of various characteristics of interest are presented. Also, we provide the coefficients which minimize the variance of the l-step GCE. Minimizing a weighted sum of variances of all concerned estimators of interest, we drive one set of the compromise coefficient of l-step GCE's to preserve additivity of estimates.

L-Estimation for the Parameter of the AR(l) Model (AR(1) 모형의 모수에 대한 L-추정법)

  • Han Sang Moon;Jung Byoung Cheal
    • The Korean Journal of Applied Statistics
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    • v.18 no.1
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    • pp.43-56
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    • 2005
  • In this study, a robust estimation method for the first-order autocorrelation coefficient in the time series model following AR(l) process with additive outlier(AO) is investigated. We propose the L-type trimmed least squares estimation method using the preliminary estimator (PE) suggested by Rupport and Carroll (1980) in multiple regression model. In addition, using Mallows' weight function in order to down-weight the outlier of X-axis, the bounded-influence PE (BIPE) estimator is obtained and the mean squared error (MSE) performance of various estimators for autocorrelation coefficient are compared using Monte Carlo experiments. From the results of Monte-Carlo study, the efficiency of BIPE(LAD) estimator using the generalized-LAD to preliminary estimator performs well relative to other estimators.