• Title/Summary/Keyword: Regression estimators

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Optimal fractions in terms of a prediction-oriented measure

  • Lee, Won-Woo
    • Journal of the Korean Statistical Society
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    • v.22 no.2
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    • pp.209-217
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    • 1993
  • The multicollinearity problem in a multiple linear regression model may present deleterious effects on predictions. Thus, its is desirable to consider the optimal fractions with respect to the unbiased estimate of the mean squares errors of the predicted values. Interstingly, the optimal fractions can be also illuminated by the Bayesian inerpretation of the general James-Stein estimators.

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Nonresponse Adjusted Raking Ratio Estimation

  • Park, Mingue
    • Communications for Statistical Applications and Methods
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    • v.22 no.6
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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.

A Study on the Estimation of Standard Deviation of Least Absolute Deviation Estimators of Regression Coefficients (회귀계수의 최소절대편차추정량의 표준편차 추정법)

  • 이기훈;정성석
    • The Korean Journal of Applied Statistics
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    • v.14 no.2
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    • pp.463-473
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    • 2001
  • 선형모형의 회귀계수의 L$_1$-추정량의 점근분포는 오차항의 중앙값에 종속되어있는데, 이 값은 잔차의 순서통계량의 함수로 추정될 수 있다. 본 논문에서는 오차항 중앙값의 추정량을 유도하는 몇 가지 방법을 소개하고 몬테칼로 실험을 통하여 가장 바람직한 추정량의 형태를 제안하였다. 또한 제안한 추정량을 이용하면 검정문제에서도 좋은 결과를 얻을 수 있음을 보였다.

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On a Robust Test for Parallelism of Regression Lines against Ordered Alternatives

  • Song, Moon-Sup;Kim, Jin-Ho
    • Communications for Statistical Applications and Methods
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    • v.4 no.2
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    • pp.565-579
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    • 1997
  • A robust test is proposed for the problem of testing the parallelism of several regression lines against ordered alternatives. The proposed test statistic is based on a linear combination of one-step pairwise GM-estimators. We compare the performance of the proposed test with that of the other tests through a Monte Carlo simulation. The results of the simulation study show that the proposed test has stable levels, good empirical powers in various circumstances, and particularly higher empirical powers under the presence of extreme outliers or leverage points.

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Adaptive L-estimation for regression slope under asymmetric error distributions (비대칭 오차모형하에서의 회귀기울기에 대한 적합된 L-추정법)

  • 한상문
    • The Korean Journal of Applied Statistics
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    • v.6 no.1
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    • pp.79-93
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    • 1993
  • We consider adaptive L-estimation of estimating slope parameter in regression model. The proposed estimator is simple extension of trimmed least squares estimator proposed by ruppert and carroll. The efficiency of the proposed estimator is especially well compared with usual least squares estimator, least absolute value estimator, and M-estimators designed for asymmetric distributions under asymmetric error distributions.

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Asymptotics for Accelerated Life Test Models under Type II Censoring

  • Park, Byung-Gu;Yoon, Sang-Chul
    • Journal of the Korean Data and Information Science Society
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    • v.7 no.2
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    • pp.179-188
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    • 1996
  • Accelerated life testing(ALT) of products quickly yields information on life. In this paper, we investigate asymptotic normalities of maximum likelihood(ML) estimators of parameters for ALT model under Type II censored data using results of Bhattacharyya(1985). Further illustrations include the treatment of asymptotic of the exponential and Weibull regression models.

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Weighted Least Absolute Error Estimation of Regression Parameters

  • Song, Moon-Sup
    • Journal of the Korean Statistical Society
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    • v.8 no.1
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    • pp.23-36
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    • 1979
  • In the multiple linear regression model a class of weighted least absolute error estimaters, which minimize the sum of weighted absolute residuals, is proposed. It is shown that the weighted least absolute error estimators with Wilcoxon scores are equivalent to the Koul's Wilcoxon type estimator. Therefore, the asymptotic efficiency of the proposed estimator with Wilcoxon scores relative to the least squares estimator is the same as the Pitman efficiency of the Wilcoxon test relative to the Student's t-test. To find the estimates the iterative weighted least squares method suggested by Schlossmacher is applicable.

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Usage of auxiliary variable and neural network in doubly robust estimation

  • Park, Hyeonah;Park, Wonjun
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.3
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    • pp.659-667
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    • 2013
  • If the regression model or the propensity model is correct, the unbiasedness of the estimator using doubly robust imputation can be guaranteed. Using a neural network instead of a logistic regression model for the propensity model, the estimators using doubly robust imputation are approximately unbiased even though both assumed models fail. We also propose a doubly robust estimator of ratio form using population information of an auxiliary variable. We prove some properties of proposed theory by restricted simulations.

Some Basic and Asymptotic Properies in INMA(q) Processes

  • Park, You-Sang;Kim, Myung-Jin
    • Journal of the Korean Statistical Society
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    • v.26 no.2
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    • pp.155-170
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    • 1997
  • We propose an integer-valued MA(q) process with Poisson disturbance. Its various properties are discussed such as the joint distribution, time reversibility and regression. We derive the asymptotic distribution of autocovariance function and estimators of the parameters in the suggested model. We also consider the relationship between INMA(q) and M/D/.infty. processes.

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Optimal Rates of Convergence for Tensor Spline Regression Estimators

  • Koo, Ja-Yong
    • Journal of the Korean Statistical Society
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    • v.19 no.2
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    • pp.105-112
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    • 1990
  • Let (X, Y) be a pair random variables and let f denote the regression function of the response Y on the measurement variable X. Let K(f) denote a derivative of f. The least squares method is used to obtain a tensor spline estimator $\hat{f}$ of f based on a random sample of size n from the distribution of (X, Y). Under some mild conditions, it is shown that $K(\hat{f})$ achieves the optimal rate of convergence for the estimation of K(f) in $L_2$ and $L_{\infty}$ norms.

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