• Bulletin of the Korean Mathematical Society
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• v.34 no.4
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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.

• Survey Research
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• v.5 no.2
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• pp.49-70
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• 2004

This paper is a broad review about general regression estimators, which are very useful when auxiliary variables are available in survey sampling. We investigate the process of development of general regression estimators from birth to suggestion of variance estimation method and examine some properties of general regression estimators by comparing with calibration and QR estimators. We also present some forms of general regression estimators available under complex sampling designs such as stratified sampling and cluster sampling. Finally, we comment some advantages as well as disadvantages of general regression estimators and theoretical and practical development in the future.

• Journal of applied mathematics & informatics
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• v.18 no.1_2
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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.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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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.

• Communications for Statistical Applications and Methods
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• v.4 no.2
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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.

• Communications for Statistical Applications and Methods
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• v.31 no.3
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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.

• Journal of the Korean Statistical Society
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• v.26 no.3
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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.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.265-275
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• 1993

This paper deals with the M-estimators in regression model. The class of M-estimators is defined on nonlinear regression model and the conditions to hold the consistency of the considered estimators are suggested when the parameter space of the model is compact.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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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

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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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.