• Communications for Statistical Applications and Methods
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• 제25권5호
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• pp.569-575
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• 2018

Pareto distribution is important to analyze data in actuarial sciences, reliability, finance, and climatology. In general, unknown parameters of the Pareto distribution are estimated based on the maximum likelihood method that may yield inadequate inference results for small sample sizes and high percent censored data. In this paper, a new approach based on the regression framework is proposed to estimate unknown parameters of the Pareto distribution under the progressive Type-II censoring scheme. The proposed method provides a new regression type estimator that employs the spacings of exponential progressive Type-II censored samples. In addition, the provided estimator is a consistent estimator with superior performance compared to maximum likelihood estimators in terms of the mean squared error and bias. The validity of the proposed method is assessed through Monte Carlo simulations and real data analysis.

• 대한산업공학회지
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• 제23권2호
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• pp.371-383
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• 1997

This paper propose a fuzzy regression method using fuzzy neural networks when a membership value is attached to each input-output pair. First, a method of linear fuzzy regression analysis is described by interpreting the reliability of each input-output pair as its membership values. Next, an architecture of fuzzy neural networks with fuzzy weights and fuzzy biases is shown. The fuzzy neural network maps a crisp input vector to a fuzzy output. A cost function is defined using the fuzzy output from the fuzzy neural network and the corresponding target output with a membership value. A learning algorithm is derived from the cost function. The derived learning algorithm trains the fuzzy neural network so that the level set of the fuzzy output includes the target output. Last, the proposed method is illustrated by computer simulations on numerical examples.

• 응용통계연구
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• 제29권7호
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• pp.1361-1371
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• 2016

분위수 회귀모형은 설명변수가 반응변수의 조건부 분위수 함수에 어떻게 관계되는지 탐색함으로서 많은 유용한 정보를 제공한다. 그러나 설명변수와 반응변수가 비선형 관계를 갖는다면 선형형태를 가정하는 전통적인 분위수 회귀모형은 적합하지 않다. 또한 고차원 자료 또는 설명변수간 상관관계가 높은 자료에 대해서 변수선택의 방법이 필요하다. 이러한 이유로 본 연구에서는 벌점화 분위수 회귀나무모형을 제안하였다. 한편 제안한 방법의 분할규칙은 과도한 계산시간과 분할변수 선택편향 문제를 극복한 잔차 분석을 기반으로 하였다. 본 연구에서는 모의실험과 실증 예제를 통해 제안한 방법의 우수한 성능과 유용성을 확인하였다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 춘계학술대회논문집
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• pp.629-633
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• 2005

The reduction of the Vehicle interior noise has been the main interest of NVH engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. In particular, the HVAC sound among the vehicle interior noise has been reflected sensitively in the side of psychology. In previous study, we have developed to verify identification of source for the vehicle HVAC system through multiple-dimensional spectral analysis. Also we carried out objective assessments on the vehicle HVAC noises and subjective assessments have been already performed with 30 subjects. In this study, the linear regression models were obtained for the subjective evaluation and the sound quality metrics. The regression procedure also allows you to produce diagnostic statistics to evaluate the regression estimates including appropriation and accuracy. Appropriation of regression model is necessary to $R^2$ value and F-value. And testing for regression model is necessary to Independence, Homoscedesticity and Normality. Also we selected optimum layout of damping material using Taguchi method. As a result of application, sound quality is improved by more quiet, powerful, expensive, smooth.

• 한국컴퓨터정보학회논문지
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• 제26권8호
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• pp.157-164
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• 2021

회귀분석은 반응변수와 예측변수들 간의 관련성을 설명하기 위해 사용되는 잘 알려진 통계 테크닉이다. 특히 연구자들은 두 개의 독립 모집단에서의 모형들의 회귀계수들(절편과 기울기)을 비교하는데 관심이 있다. Gregory Chow에 의해 제안된 Chow 검정은 회귀모형들을 비교하고 선형회귀모형 안에 구조적 브레이크가 존재하는지를 검정하기 위해 보통 사용되는 방법들 중의 하나이다. 본 연구에서는 두 독립 선형회귀모형들의 등가성을 검정하기 위해 퍼뮤테이션 방법을 제안하고 Chow 검정과 비교한다. 그리고 퍼뮤테이션 검정과 Chow 검정의 검정력을 조사하기 위해 시물레이션 연구를 진행하였다.

• Communications for Statistical Applications and Methods
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• 제5권3호
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• pp.733-742
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• 1998

For industrial and medical lifetime data, the generalized log-gamma regression model is considered. Then the Bayesian analysis for the generalized log-gamma regression with censored data are explained and following the data augmentation (Tanner and Wang; 1987), the censored data is replaced by simulated data. To overcome the complicated Bayesian computation, Makov Chain Monte Carlo (MCMC) method is employed. Then some modified algorithms are proposed to implement MCMC. Finally, one example is presented.

• Journal of applied mathematics & informatics
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• 제8권3호
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• pp.869-880
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• 2001

In this paper, we describe a method for fuzzy polynomial regression analysis for fuzzy input-output data using shape preserving operations based on Tanaka’s approach. Shape preserving operations simplifies the computation of fuzzy arithmetic operations. We derive the solution using general linear program.

• 한국지능시스템학회논문지
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• 제13권5호
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• pp.571-575
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• 2003

In this paper, we describe a method for fuzzy polynomial regression analysis for fuzzy input--output data using shape preserving operations for least-squares fitting. Shape preserving operations simplifies the computation of fuzzy arithmetic operations. We derive the solution using mixed nonlinear program.

• 가정과삶의질연구
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• 제10권1호통권19호
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• pp.131-138
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• 1992

Regression analysis has become a standard statistical tool in the behavioral science. Because of its widespread popularity. regression has been often misused. Such is the case when the dependent variable is a qualitative measure rather than a continuous, interval measure. Regression estimates with a qualitative dependent variable does not meet the assumptions underlying regression. It can lead to serious errors in the standard statistical inference. Logit model is recommended as alternatives to the regression model for qualitative dependent variables. Researchers can employ this model to measure the relationship between independent variables and qualitative dependent variables without assuming that logit model was derived from probabilistic choice theory. Coefficients in logit model are typically estimated by the method of Maximum Likelihood Estimation in contrast to ordinary regression model which estimated by the method of Least Squares Estimation. Goodness of fit in logit model is based on the likelihood ratio statistics and the t-statistics is used for testing the null hypothesis.

• Communications for Statistical Applications and Methods
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• 제16권2호
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• pp.335-348
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• 2009

Fuzzy regression is used as a complement or an alternative to represent the relation between variables among the forecasting models especially when the data is insufficient to evaluate the relation. Such phenomenon often occurs in seasonal time series data which require large amount of data to describe the underlying pattern. Semiparametric model is useful tool in the case where domain knowledge exists about the function to be estimated or emphasis is put onto understandability of the model. In this paper we propose fuzzy semiparametric support vector regression so that it can provide good performance on forecasting of the seasonal time series by incorporating into fuzzy support vector regression the basis functions which indicate the seasonal variation of time series. In order to indicate the performance of this method, we present two examples of predicting the seasonal time series. Experimental results show that the proposed method is very attractive for the seasonal time series in fuzzy environments.