• The Korean Journal of Applied Statistics
• /
• v.14 no.1
• /
• pp.71-80
• /
• 2001

Logistic regression model is one of the most popular linear models for a binary response variable and used for the estimation of probability function. In many practical situations, the probability function can be expressed by a bell shaped curve and such a function can be estimated by a second order logistic regression model. However, when the probability curve is asymmetric, the estimation results using a second order logistic regression model may not be precise because a second order logistic regression model is a symmetric function. In addition, even if a second order logistic regression model is used, the interpretation for the effect of second order term may not be easy. In this paper, in order to alleviate such problems, an estimation method for asymmetric probabiity curve based on a first order logistic regression model and iterative bi-section method is proposed and its performance is compared with that of a second order logistic regression model by a simulation study.

• Journal of the Society of Naval Architects of Korea
• /
• v.51 no.6
• /
• pp.530-538
• /
• 2014

The weight estimation of floating offshore structures such as FPSO, TLP, semi-Submersibles, Floating Offshore Wind Turbines etc. in the preliminary design, is one of important measures of both construction cost and basic performance. Through both literature investigation and internet search, the weight data of floating offshore structures such as FPSO and TLP was collected. In this study, the weight estimation model was suggested for FPSO. The weight estimation model using non-linear regression analysis was established by fixing independent variables based on this data and the multiple regression analysis was introduced into the weight estimation model. Its reliability was within 4% of error rate.

• Korean Journal of Ecology and Environment
• /
• v.57 no.2
• /
• pp.111-122
• /
• 2024

Zooplankton biomass is essential for understanding the quantitative structure of lake food webs and for the functional assessment of biotic interactions. In this study, we aimed to propose a biomass (dry weight) estimation method using the body length of cyclopoid copepods. These copepods play an important role as omnivores in lake zooplankton communities and contribute significantly to biomass. We validated several previously proposed estimation equations against direct measurements and compared the suitability of prosomal length versus total length of copepods to suggest a more appropriate estimation equation. After comparing the regression analysis results of various candidate equations with the actual values measured on a microbalance-using the coefficient of variation, mean absolute error, and coefficient of determination-it was determined that the Total Length-DW exponential regression equation [W=0.7775×e^2.0183L; W (㎍), L (mm)] could be used to calculate biomass with higher accuracy. However, considering practical issues such as the morphological similarity between species and genera of copepods and the limitations of classifying copepodid stages, we derived a general regression equation for the pooled copepod community rather than a species-specific regression equation.

• The Korean Journal of Applied Statistics
• /
• v.23 no.6
• /
• pp.1245-1253
• /
• 2010

There are two main approaches for estimating the discontinuous regression function nonparametrically. One is the direct approach, the other is the indirect approach. The major goal of the two approaches are different. The direct approach focuses on the overall good estimation of the regression function itself, whereas the indirect approach focuses on the good estimation of jump locations. Apparently, the two approaches are quite different in nature. Gijbels et al. (2007) argue that the comparison of two approaches does not make much sense and that it is even difficult to choose an appropriate criterion for comparisons. However, it is obvious that the indirect approach also has the regression curve estimate as the subsidiary result. Therefore it is necessary to verify the appropriateness of the indirect approach as the estimator of the discontinuous regression function itself. Park (2009a) compared the performance of two approaches through a simulation study. In this paper, we consider a more general case and draw some useful conclusions.

• East Asian mathematical journal
• /
• v.14 no.2
• /
• pp.421-427
• /
• 1998

In this paper we provide sufficient conditions which ensure the strong consistency of $L_p$-norm estimation in nonlinear regression model when the probability distribution of the errors term is symmetric about zero. The least absolute deviation and least square estimation are discussed as special cases of the proposed estimation.

• Communications for Statistical Applications and Methods
• /
• v.19 no.3
• /
• pp.423-432
• /
• 2012

This paper is concerned with the detection of multiple change-points in linear regression models. The proposed procedure relies on the local estimation for global change-point estimation. We propose a multiple change-point estimator based on the local least squares estimators for the regression coefficients and the split measure when the number of change-points is unknown. Its statistical properties are shown and its performance is assessed by simulations and real data applications.

• Proceedings of the Korean Statistical Society Conference
• /
• 2002.11a
• /
• pp.103-108
• /
• 2002

We consider an estimation of discontinuous variance function in nonparametric heteroscedastic random design regression model. We first propose estimators of a change point and jump size in variance function and then construct an estimator of entire variance function. We examine the rates of convergence of these estimators and give results on their asymptotics. Numerical work reveals that the effectiveness of change point analysis in variance function estimation is quite significant.

• Journal of the Korean Data and Information Science Society
• /
• v.5 no.1
• /
• pp.11-20
• /
• 1994

In recent years, nonparametric kernel estimation of regresion function are abundant and widely applicable to many areas of statistics. Most of modern researches concerned with the fixed global bandwidth selection which can be used in the estimation of regression function with all the same value for all x. In this paper, we propose a method for selecting locally varing bandwidth based on bootstrap method in kernel estimation of fixed design regression. Performance of proposed bandwidth selection method for finite sample case is conducted via Monte Carlo simulation study.

• Journal of the Korean Data and Information Science Society
• /
• v.14 no.2
• /
• pp.337-343
• /
• 2003

In this paper we present the prediction interval estimation method using bootstrap method for least squares support vector machine(LS-SVM) regression, which allows us to perform even nonlinear regression by constructing a linear regression function in a high dimensional feature space. The bootstrap method is applied to generate the bootstrap sample for estimation of the covariance of the regression parameters consisting of the optimal bias and Lagrange multipliers. Experimental results are then presented which indicate the performance of this algorithm.

• Journal of the Korean Data and Information Science Society
• /
• v.9 no.2
• /
• pp.165-172
• /
• 1998

This paper considers a linear regression model with censored data where each error term follows a multivariate normal distribution. In this paper we consider the diffuse prior distribution for parameters of the linear regression model. With censored data we derive the full conditional densities for parameters of a multiple regression model in order to obtain the marginal posterior densities of the relevant parameters through the Gibbs Sampler, which was proposed by Geman and Geman(1984) and utilized by Gelfand and Smith(1990) with statistical viewpoint.