• Journal of the Korean Data and Information Science Society
• /
• 제26권1호
• /
• pp.209-216
• /
• 2015

Quantile regression models with covariate measurement errors have received a great deal of attention in both the theoretical and the applied statistical literature. A lot of effort has been devoted to develop effective estimation methods for such quantile regression models. In this paper we propose the partially linear support vector orthogonal quantile regression model in the presence of covariate measurement errors. We also provide a generalized approximate cross-validation method for choosing the hyperparameters and the ratios of the error variances which affect the performance of the proposed model. The proposed model is evaluated through simulations.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국제학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
• /
• pp.1041-1044
• /
• 1988

This paper considers the design problem of adaptive filters based an the state-space models for linear discrete-time stationary stochastic signal processes. The adaptive state estimator consists of both the predictor and the sequential prediction error estimator. The discrete Chandrasakhar filter developed by author is employed as the predictor and the nonlinear least-squares estimator is used as the sequential prediction error estimator. Two models are presented for calculating the parameter sensitivity functions in the adaptive filter. One is the exact model called the linear innovations model and the other is the simplified model obtained by neglecting the sensitivities of the Chandrasekhar X and Y functions with respect to the unknown parameters in the exact model.

• Wind and Structures
• /
• 제27권1호
• /
• pp.59-70
• /
• 2018

In this paper, geometrically non-linear analysis of a functionally graded simple supported beam is investigated with porosity effect. The material properties of the beam are assumed to vary though height direction according to a prescribed power-law distributions with different porosity models. In the nonlinear kinematic model of the beam, the total Lagrangian approach is used within Timoshenko beam theory. In the solution of the nonlinear problem, the finite element method is used in conjunction with the Newton-Raphson method. In the study, the effects of material distribution such as power-law exponents, porosity coefficients, nonlinear effects on the static behavior of functionally graded beams are examined and discussed with porosity effects. The difference between the geometrically linear and nonlinear analysis of functionally graded porous beam is investigated in detail. Also, the effects of the different porosity models on the functionally graded beams are investigated both linear and nonlinear cases.

• Communications for Statistical Applications and Methods
• /
• 제19권6호
• /
• pp.809-817
• /
• 2012

We propose a semiparametric inference for a generalized varying coefficient partially linear model(VCPLM) for negative binomial data. The VCPLM is useful to model real data in that varying coefficients are a special type of interaction between explanatory variables and partially linear models fit both parametric and nonparametric terms. The negative binomial distribution often arise in modelling count data which usually are overdispersed. The varying coefficient function estimators and regression parameters in generalized VCPLM are obtained by formulating a penalized likelihood through smoothing splines for negative binomial data when the shape parameter is known. The performance of the proposed method is then evaluated by simulations.

• 한국측량학회:학술대회논문집
• /
• 한국측량학회 2004년도 추계학술발표회 논문집
• /
• pp.179-184
• /
• 2004

High-resolution satellite imagery are used in various application field such as generation of DEM, orthophto, and three dimensional city model. To define the relation between image and object space, sensor modelling and generation of the epipolar image is essential processes. As the header information or physical sensor model becomes unavailable for the end users due to the national security or commercial purpose, generation of epipolar images without these information becomes one of important processes. In this study, epipolar geometry is generated and analysed by applying two generalized sensor models; parallel and parallel-perspective model Epipolar equation of the parallel model has linear property which is relatively simple; Epipolar geometry of the parallel-perspective model is non-linear. This linear property enable us to generate epipolar image efficiently.

• 한국철도학회논문집
• /
• 제9권1호
• /
• pp.63-68
• /
• 2006

This paper proposes a fast Fourier transform(FFT)-based spectral analysis method(SAM) for the dynamic responses of the linear discrete dynamic models with non-proportional damping. The SAM was developed by using discrete Fourier transform(DFT)-theory. To verify the proposed SAM, a three-DOF system with non-proportional viscous damping is considered as an illustrative example. The present SAM is evaluated by comparing the dynamic responses obtained by SAM with those obtained by Runge-Kutta method.

• 한국정보시스템학회지:정보시스템연구
• /
• 제4권
• /
• pp.27-39
• /
• 1995

This study tests the fuzzy linear regression model to see if there is a performance difference between it and the classical linear regression model. These results show that FLR was better as f forecasting technique when compared with CLR. Another important find in the test of the two different regression methods is that they generate two different predicted P/E ratios from expected value test, variance test and error test of two different regressions, though we can not see a significant difference between two regression models doing test in error measurements (GMRAE, MAPE, MSE, MAD). So, in this financial setting we can conclude that FLR is not superior to CLR, comparing and testing between the t재 different regression models. However, FLR is better than CLR in the error measurements.

• 음성과학
• /
• 제11권4호
• /
• pp.75-88
• /
• 2004

In this paper, we propose a new approach to sequential linear regression adaptation of continuous density hidden Markov models (CDHMMs) based on transformation space model (TSM). The proposed TSM which characterizes the a priori knowledge of the training speakers associated with maximum likelihood linear regression (MLLR) matrix parameters is effectively described in terms of the latent variable models. The TSM provides various sources of information such as the correlation information, the prior distribution, and the prior knowledge of the regression parameters that are very useful for rapid adaptation. The quasi-Bayes (QB) estimation algorithm is formulated to incrementally update the hyperparameters of the TSM and regression matrices simultaneously. Experimental results showed that the proposed TSM approach is better than that of the conventional quasi-Bayes linear regression (QBLR) algorithm for a small amount of adaptation data.

• Communications for Statistical Applications and Methods
• /
• 제13권2호
• /
• pp.297-307
• /
• 2006

Most methods for describing the relationship among random variables require specific probability distributions and some assumptions of random variables. The mutual information based on the entropy to measure the dependency among random variables does not need any specific assumptions. And the redundancy which is a analogous version of the mutual information was also proposed. In this paper, the redundancy and mutual information are explored to multi-dimensional categorical data. It is found that the redundancy for categorical data could be expressed as the function of the generalized likelihood ratio statistic under several kinds of independent log-linear models, so that the redundancy could also be used to analyze contingency tables. Whereas the generalized likelihood ratio statistic to test the goodness-of-fit of the log-linear models is sensitive to the sample size, the redundancy for categorical data does not depend on sample size but its cell probabilities itself.

• 응용통계연구
• /
• 제17권1호
• /
• pp.1-12
• /
• 2004

본 논문에서는 2001년 경기도 양평 지역의 주민 880여명을 대상으로 반복측정되어 얻어진 혈압자료를 이용하여 반복요인이 세 개인 경우에 적절한 공분산구조를 선택하는 방법에 대해서 알아보고 혼합선형모형(mixed linear model)을 이용한 분석을 통해서 혈압에 영향을 주는 공변량(covariates)에 대해서 알아보았다.