• 제목/요약/키워드: Regression estimators

검색결과 226건 처리시간 0.023초

The strong consistency of the $L_1$-norm estimators in censored nonlinear regression models

  • Park, Seung-Hoe;Kim, Hae-Kyung
    • 대한수학회보
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    • 제34권4호
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    • pp.573-581
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    • 1997
  • This paper is concerned with the strong consistency of the $L_1$-norm estimators for the nonlinear regression models when dependent variables are subject to censoring, and provides the sufficient conditions which ensure the strong consistency of $L_1$-norm estimators of the censored regression models.

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표본조사에서 일반회귀 추정량의 활용 (General Regression Estimators in Survey Sampling)

  • 김규성
    • 한국조사연구학회지:조사연구
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    • 제5권2호
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    • pp.49-70
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    • 2004
  • 표본조사에서 사용 가능한 보조변수가 있는 경우에 추정의 효율을 높이기 위하여 보조변수를 활용하는 방법이 다각적으로 개발되어 왔다. 이 논문은 보조변수를 효과적으로 이용하는 방법 중의 하나인 일반회귀추정량에 대한 개괄적인 고찰이다. 일반회귀추정량의 출현부터 분산추정법의 제안까지 이론전개 과정을 살펴보았으며, 보정추정량 및 QR추정량과의 관련성을 통하여 일반회귀추정량의 성질을 알아보았다. 특히 분산추정에서 통상적인 설계기반 분산추정량이 가지는 조건부 성질의 약점을 보완하기 위하여 가중잔차기법을 사용하는 과정을 살펴보았다. 층화표집이나 집략표집과 같은 복합설계에서 활용할 수 있는 일반회귀추정량의 형태를 소개하였고, 마지막으로 일반회귀추정량의 장단점, 그리고 향후 이론적인 발전방향 및 실용적인 발전방향을 언급하였다.

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ROBUST FUZZY LINEAR REGRESSION BASED ON M-ESTIMATORS

  • SOHN BANG-YONG
    • Journal of applied mathematics & informatics
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    • 제18권1_2호
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    • pp.591-601
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    • 2005
  • The results of fuzzy linear regression are very sensitive to irregular data. When this points exist in a set of data, a fuzzy linear regression model can be incorrectly interpreted. The purpose of this paper is to detect irregular data and to propose robust fuzzy linear regression based on M-estimators with triangular fuzzy regression coefficients for crisp input-output data. Numerical example shows that irregular data can be detected by using the residuals based on M-estimators, and the proposed robust fuzzy linear regression is very resistant to this points.

Regression Quantile Estimators of a Nonlinear Time Series Regression Model

  • 김태수;허선;김해경
    • 한국통계학회:학술대회논문집
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    • 한국통계학회 2000년도 추계학술발표회 논문집
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    • pp.13-15
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    • 2000
  • In this paper, we deal with the asymptotic properties of the regression quantile estimators in the nonlinear time series regression model. For the sinusodial model which frequently appears fer a time series analysis, we study the strong consistency and asymptotic normality of regression quantile ostinators.

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Exact Confidence Intervals on the Regression Coeffcients in Multiple Regression Model with Nested Error Structure

  • Park, Dong-Joon
    • Communications for Statistical Applications and Methods
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    • 제4권2호
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    • pp.541-548
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    • 1997
  • In regression model with nested error structure interval estimations on regression coefficients in different stages are proposed. Ordinary least square estimators and generalized least square estimators of the regression coefficients in this model are derived for between and within group model. The confidence intervals are dervied by using independent idstributional properties between regression coefficient estimators and quadratic froms obtained from the model.

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Some efficient ratio-type exponential estimators using the Robust regression's Huber M-estimation function

  • Vinay Kumar Yadav;Shakti Prasad
    • Communications for Statistical Applications and Methods
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    • 제31권3호
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    • pp.291-308
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    • 2024
  • The current article discusses ratio type exponential estimators for estimating the mean of a finite population in sample surveys. The estimators uses robust regression's Huber M-estimation function, and their bias as well as mean squared error expressions are derived. It was campared with Kadilar, Candan, and Cingi (Hacet J Math Stat, 36, 181-188, 2007) estimators. The circumstances under which the suggested estimators perform better than competing estimators are discussed. Five different population datasets with a well recognized outlier have been widely used in numerical and simulation-based research. These thorough studies seek to provide strong proof to back up our claims by carefully assessing and validating the theoretical results reported in our study. The estimators that have been proposed are intended to significantly improve both the efficiency and accuracy of estimating the mean of a finite population. As a result, the results that are obtained from statistical analyses will be more reliable and precise.

Robust Regression and Stratified Residuals for Left-Truncated and Right-Censored Data

  • Kim, Chul-Ki
    • Journal of the Korean Statistical Society
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    • 제26권3호
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    • pp.333-354
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    • 1997
  • Computational algorithms to calculate M-estimators and rank estimators of regression parameters from left-truncated and right-censored data are developed herein. In the case of M-estimators, new statistical methods are also introduced to incorporate leverage assements and concomitant scale estimation in the presence of left truncation and right censoring on the observed response. Furthermore, graphical methods to examine the residuals from these data are presented. Two real data sets are used for illustration.

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On Confidence Intervals of High Breakdown Regression Estimators

  • Lee Dong-Hee;Park YouSung;Kim Kang-yong
    • 한국통계학회:학술대회논문집
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    • 한국통계학회 2004년도 학술발표논문집
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    • pp.205-210
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    • 2004
  • A weighted self-tuning robust regression estimator (WSTE) has the high breakdown point for estimating regression parameters such as other well known high breakdown estimators. In this paper, we propose to obtain standard quantities like confidence intervals, and it is found to be superior to the other high breakdown regression estimators when a sample is contaminated

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Self-tuning Robust Regression Estimation

  • Park, You-Sung;Lee, Dong-Hee
    • 한국통계학회:학술대회논문집
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    • 한국통계학회 2003년도 추계 학술발표회 논문집
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    • pp.257-262
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    • 2003
  • We introduce a new robust regression estimator, self-tuning regression estimator. Various robust estimators have been developed with discovery for theories and applications since Huber introduced M-estimator at 1960's. We start by announcing various robust estimators and their properties, including their advantages and disadvantages, and furthermore, new estimator overcomes drawbacks of other robust regression estimators, such as ineffective computation on preserving robustness properties.

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