• 응용통계연구
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• 제29권1호
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• pp.267-277
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• 2016

본 연구에서는 잔차를 이용하여 오차항의 코플라 함수를 추정하는 문제를 고려하였다. 확률적 회귀모형을 개별모형으로 갖는 경우, 오차항 대신 잔차들의 경험적 분포함수를 이용하여 구한 코플라 모수에 대한 준모수적 추정량의 성질을 살펴보았으며, 이 추정량이 일치추정량이 되기 위한 조건을 구하였다. 응용사례로 코플라-자기회귀이동평균 모형을 다루었으며, 모의실험을 통해 자기회귀 근사를 통해 얻은 잔차를 이용하여 계산한 추정량의 성질도 살펴보았다.

• Journal of the Korean Data and Information Science Society
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• 제21권4호
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• pp.765-775
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• 2010

For the accelerated failure time (AFT) model a lot of effort has been devoted to develop effective estimation methods. AFT model assumes a linear relationship between the logarithm of event time and covariates. In this paper we propose a semiparametric support vector machine to consider situations where the functional form of the effect of one or more covariates is unknown. The proposed estimating equation can be computed by a quadratic programming and a linear equation. We study the effect of several covariates on a censored response variable with an unknown probability distribution. We also provide a generalized approximate cross-validation method for choosing the hyper-parameters which affect the performance of the proposed approach. The proposed method is evaluated through simulations using the artificial example.

• Journal of the Korean Data and Information Science Society
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• 제27권5호
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• pp.1433-1441
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• 2016

Small area model provides reliable and accurate estimations when the sample size is not sufficient. Our dataset has an inherent nonlinear pattern which signicantly affects our inference. In this case, we could consider semiparametric models such as truncated polynomial basis function and radial basis function. In this paper, we study four Bayesian semiparametric models for small areas to handle this point. Four small area models are based on two kinds of basis function and different knots positions. To evaluate the different estimates, four comparison measurements have been employed as criteria. In these comparison measurements, the truncated polynomial basis function with equal quantile knots has shown the best result. In Bayesian calculation, we use Gibbs sampler to solve the numerical problems.

• Communications for Statistical Applications and Methods
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• 제22권4호
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• pp.349-359
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• 2015

We study a semiparametric Bayesian approach to small area estimation under a nested error linear regression model with area level covariate subject to measurement error. Consideration is given to radial basis functions for the regression spline and knots on a grid of equally spaced sample quantiles of covariate with measurement errors in the nested error linear regression model setup. We conduct a hierarchical Bayesian structural measurement error model for small areas and prove the propriety of the joint posterior based on a given hierarchical Bayesian framework since some priors are defined non-informative improper priors that uses Markov Chain Monte Carlo methods to fit it. Our methodology is illustrated using numerical examples to compare possible models based on model adequacy criteria; in addition, analysis is conducted based on real data.

• 자원ㆍ환경경제연구
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• 제14권2호
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• pp.447-474
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• 2005

응답자들에 따라 지불의사액(willingness to pay : WTP) 조사에서 응답한 WTP에 대한 확신도, 즉 선호의 강도가 다를 수 있다. 본 연구는 선호강도의 정도에 대한 정보를 얻기 위해 응답자가 응답한 WTP에 대해 선호강도가 어떤지에 대한 응답을 이끌어 내었다. 선호강도를 반영한 WTP 자료의 분석을 위해 본 논문에서는 Type 3 토빗모형의 적용을 고려한다. 이 모형을 추정하기 위해서는 통상 동분산 및 이변량 정규성을 만족하는 오차항 구조를 가정한 모수적 2단계 추정법을 적용한다. 하지만 이 가정들이 만족되지 않는다면 추정치는 비일치적이게 된다. 동분산과 정규성 가설에 대해 검정한 결과 유의수준 1%에서 두 가정은 모두 기각되었다. 따라서 모수적 Type 3 토빗모형을 추정하는데 요구되는 가정은 너무 제약적이라 할 수 있다. 본 연구에서는 이 모수적 모형에 대한 대안으로 준모수적 Type 3 토빗모형을 적용한다. 분석결과 준모수적 추정은 모수적 추정보다 유의하게 우수하였으며, 더욱더 중요하게는 모수적 모형으로부터 계산된 평균 WTP 추정치는 준모수적 모형으로부터 계산된 것과 유의하게 다름을 알 수 있었다.

• Journal of the Korean Statistical Society
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• 제24권1호
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• pp.149-160
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• 1995

We consider the problem of optimal adaptive estiamtion of the euclidean parameter vector $\theta$ of the univariate non-linerar autogressive time series model ${X_t}$ which is defined by the following system of stochastic difference equations ; $X_t = \sum^p_{i=1} \theta_i \cdot T_i(X_{t-1})+e_t, t=1, \cdots, n$, where $\theta$ is the unknown parameter vector which descrives the deterministic dynamics of the stochastic process ${X_t}$ and ${e_t}$ is the sequence of white noises with unknown density $f(\cdot)$. Under some general growth conditions on $T_i(\cdot)$ which guarantee ergodicity of the process, we construct a sequence of adaptive estimatros which is locally asymptotic minimax (LAM) efficient and also attains the least possible covariance matrix among all regular estimators for arbitrary symmetric density.

• 자원ㆍ환경경제연구
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• 제3권1호
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• pp.183-198
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• 1993

• Communications for Statistical Applications and Methods
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• 제27권3호
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• pp.349-363
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• 2020

Penalized spline is a suitable nonparametric approach in estimating mean model in small area. However, application of the approach in informative sampling in a published article is uncommon. We propose a semiparametric mixed-model using penalized spline under informative sampling to estimate mean of small area. The response variable is explained in terms of mean model, informative sample effect, area random effect and unit error. We approach the mean model by penalized spline and utilize a penalized spline function of the inclusion probability to account for the informative sample effect. We determine the best and unbiased estimators for coefficient model and derive the restricted maximum likelihood estimators for the variance components. A simulation study shows a decrease in the average absolute bias produced by the proposed model. A decrease in the root mean square error also occurred except in some quadratic cases. The use of linear and quadratic penalized spline to approach the function of the inclusion probability provides no significant difference distribution of root mean square error, except for few smaller samples.

• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.401-409
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• 2006

We consider a generalized partly-parametric additive risk model which generalizes the partly parametric additive risk model suggested by McKeague and Sasieni (1994). As an estimation method of this model, we propose to use the weighted least square estimation, suggested by Huffer and McKeague (1991), for Aalen's additive risk model by a piecewise constant risk. We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least squares method.

• Communications for Statistical Applications and Methods
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• 제15권6호
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• pp.1003-1011
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• 2008

Mixed-effect Poisson regression models are widely used for analysis of correlated count data such as those found in longitudinal studies. In this paper, we consider kernel extensions with semiparametric fixed effects and parametric random effects. The estimation is through the penalized likelihood method based on kernel trick and our focus is on the efficient computation and the effective hyperparameter selection. For the selection of hyperparameters, cross-validation techniques are employed. Examples illustrating usage and features of the proposed method are provided.