• Communications for Statistical Applications and Methods
• /
• 제6권3호
• /
• pp.849-860
• /
• 1999

This paper addresses the issue of estimating regression function with errors in variables using wavelets. We adopt a nonparametric approach in assuming that the regression function has no specific parametric form, To account for errors in covariates deconvolution is involved in the construction of a new class of linear wavelet estimators. using the wavelet characterization of Besov spaces the question of regression estimation with Besov constraint can be reduced to a problem in a space of sequences. Rates of convergence are studied over Besov function classes $B_{spq}$ using $L_2$ error measure. It is shown that the rates of convergence depend on the smoothness s of the regression function and the decay rate of characteristic function of the contaminating error.

• 품질경영학회지
• /
• 제22권3호
• /
• pp.99-110
• /
• 1994

The strength-stress model has been widely used in a variety of areas including testing the reliability of the item or design procedures. This model was first introduced in 1950's and can be found on various applications in civil, aerospace engineering etc. This paper considers the strength-stress model in detail and proposes an estimator which deals with the reliability estimation problem based on censored observations in the strength variables.

• Communications for Statistical Applications and Methods
• /
• 제29권6호
• /
• pp.641-653
• /
• 2022

Penalized least squares methods are important tools to simultaneously select variables and estimate parameters in linear regression. The penalized maximum likelihood can also be used for the same purpose assuming that the error distribution falls in a certain parametric family of distributions. However, the use of a certain parametric family can suffer a misspecification problem which undermines the estimation accuracy. To give sufficient flexibility to the error distribution, we propose to use the symmetric log-concave error distribution with LASSO penalty. A feasible algorithm to estimate both nonparametric and parametric components in the proposed model is provided. Some numerical studies are also presented showing that the proposed method produces more efficient estimators than some existing methods with similar variable selection performance.

• 응용통계연구
• /
• 제29권3호
• /
• pp.449-462
• /
• 2016

본 논문은 시계열 분석의 추론에서 매우 중요한 역할을 하는 장기적 분산에 대해서 붓스트랩을 이용한 추정을 다룬다. 본 논문은 기존의 방법을 두가지 측면에서 확장한다. 첫째, 단기억 시계열에서의 장기적 분산 추정을 확장하여 자료의 의존성이 매우 강한 장기간 의존 시계열에서 붓스트랩을 이용한 장기적 분산의 추정에 대해서 논의한다. 또한 장기간 의존 시계열이 평균변화모형과 매우 쉽게 잘 혼동됨이 잘 알려져 있기에 이를 해결하기 위해서 쌍봉형 커널을 이용한 추세 추정 및 붓스트랩의 블럭을 결정하는 방법을 제안한다. 모의 실험결과 제안한 방법이 매우 유의하였으며 북반구 평균 온도 변화 자료 분석으로 실증 자료 예제도 아울러 제시하였다.

• Journal of Information Technology Applications and Management
• /
• 제27권6호
• /
• pp.153-170
• /
• 2020

The Educational Restitution Rate is an important parameter that determines the quality of university education. This paper analyzed data from 148 private universities over the 10 years from 2009 to 2018 using data mining techniques in Korea. A significant causal relationship is detected in the fixed effect model as a result of the panel estimation. And the scale of faculty expansion and fund management, which are the university evaluation indicators, and the size of basic funds, respectively, have a positive effect on the ERR, which is within the confidence interval. In the analysis, the more private universities improve the tuition dependence rate, the more decisively positive affecting ERR. As a result of nonparametric regression estimation, when the faculty expansion ratio is reinforced, the effect of economies of scale is detected in some sections, the improvement of the tuition dependence rate, and the result value is generated through the improvement that results are derived at a certain point in time. We hope that the university based on this study can be a basic Indicators for the diagnosis of basic competencies and policy of student-centered education.

• Asian-Australasian Journal of Animal Sciences
• /
• 제22권1호
• /
• pp.124-130
• /
• 2009

Estimating genetic interaction effects in animal genomics would be one of the most challenging studies because the phenotypic variation for economically important traits might be largely explained by interaction effects among multiple nucleotide sequence variants under various environmental exposures. Genetic improvement of economic animals would be expected by understanding multi-locus genetic interaction effects associated with economic traits. Most analyses in animal breeding and genetics, however, have excluded the possibility of genetic interaction effects in their analytical models. This review discusses a historical estimation of the genetic interaction and difficulties in analyzing the interaction effects. Furthermore, two recently developed methods for assessing genetic interactions are introduced to animal genomics. One is the restricted partition method, as a nonparametric grouping-based approach, that iteratively utilizes grouping of genotypes with the smallest difference into a new group, and the other is the Bayesian method that draws inferences about the genetic interaction effects based on their marginal posterior distributions and attains the marginalization of the joint posterior distribution through Gibbs sampling as a Markov chain Monte Carlo. Further developing appropriate and efficient methods for assessing genetic interactions would be urgent to achieve accurate understanding of genetic architecture for complex traits of economic animals.

• Communications for Statistical Applications and Methods
• /
• 제23권5호
• /
• pp.385-397
• /
• 2016

The stress-strength models have been intensively investigated in the literature in regards of estimating the reliability ${\theta}$ = P(X > Y) using parametric and nonparametric approaches under different sampling schemes when X and Y are independent random variables. In this paper, we consider the problem of estimating ${\theta}$ when (X, Y) are dependent random variables with a bivariate underlying distribution. The empirical and kernel estimates of ${\theta}$ = P(X > Y), based on bivariate ranked set sampling (BVRSS) are considered, when (X, Y) are paired dependent continuous random variables. The estimators obtained are compared to their counterpart, bivariate simple random sampling (BVSRS), via the bias and mean square error (MSE). We demonstrate that the suggested estimators based on BVRSS are more efficient than those based on BVSRS. A simulation study is conducted to gain insight into the performance of the proposed estimators. A real data example is provided to illustrate the process.

• Communications for Statistical Applications and Methods
• /
• 제27권3호
• /
• pp.349-363
• /
• 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 Electrical Engineering and Technology
• /
• 제9권2호
• /
• pp.461-470
• /
• 2014

This paper presents a probabilistic power flow (PPF) analysis method for distribution network incorporating the randomness and correlation of photovoltaic (PV) generation. Based on the multivariate kernel density estimation theory, the probabilistic model of PV generation is proposed without any assumption of theoretical parametric distribution, which can accurately capture not only the randomness but also the correlation of PV resources at adjacent locations. The PPF method is developed by combining the proposed PV model and Monte Carlo technique to evaluate the influence of the randomness and correlation of PV generation on the performance of distribution networks. The historical power output data of three neighboring PV generators in Oregon, USA, and 34-bus/69-bus radial distribution networks are used to demonstrate the correctness, effectiveness, and application of the proposed PV model and PPF method.

• 응용통계연구
• /
• 제34권6호
• /
• pp.937-944
• /
• 2021

본 연구는 부분선형모형에서 변수선택의 문제를 다룬다. 부분선형모형은 평활화모수 추정과 같은 비모수 추정과 선형설명변수에 대한 추정의 문제를 함께 포함하고 있어 변수선택이 쉽지 않다. 본 연구에서는 빠른 전진선택법인 LARS 를 이용한 변수선택법을 제시한다. 제안된 방법은 LARS에 의하여 선별된 변수들에 대하여 t-검정, 가능한 모든 회귀모형 비교 또는 단계별 선택법을 적용한다. 제안된 방법들의 효율성을 비교하기 위하여 실제데이터에 적용한 예제와 모의실험 결과가 제시된다.