• 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.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• Computers and Concrete
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• 제21권4호
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• pp.463-470
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• 2018

In the present study, soft computing i.e., machine learning techniques and regression models algorithms have earned much importance for the prediction of the various parameters in different fields of science and engineering. This paper depicts that how regression models can be implemented for the prediction of compressive strength of concrete. Three models are taken into consideration for this; they are Gaussian Process for Regression (GPR), Multi Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR). Contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and age in days have been taken as inputs and compressive strength as output for GPR, MARS and MPMR models. A comparatively large set of data including 1030 normalized previously published results which were obtained from experiments were utilized. Here, a comparison is made between the results obtained from all the above mentioned models and the model which provides the best fit is established. The experimental results manifest that proposed models are robust for determination of compressive strength of concrete.

• 응용통계연구
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• 제22권1호
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• pp.115-127
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• 2009

The purpose of statistical analyses of array-CGH experiment data is to divide the whole genome into regions of equal copy number, to quantify the copy number in each region and finally to evaluate its significance of being different from two. Several statistical procedures have been proposed which include the circular binary segmentation, and a Gaussian based local regression for detecting break points (GLAD) by estimating a piecewise constant function. We propose in this note a penalized spline regression and its simultaneous confidence band(SCB) approach to evaluate the statistical significance of regions of genetic gain/loss. The region of which the simultaneous confidence band stays above 0 or below 0 can be considered as a region of genetic gain or loss. We compare the performance of the SCB procedure with GLAD and hidden Markov model approaches through a simulation study in which the data were generated from AR(1) and AR(2) models to reflect spatial dependence of the array-CGH data in addition to the independence model. We found that the SCB method is more sensitive in detecting the low level copy number alterations.

• 한국운동역학회지
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• 제14권3호
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• pp.301-311
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• 2004

The purpose of this study was to verify the usefulness of the Mathcad program as a tool for the studying and teaching the sport biomechanics. A projectile motion was analyzed because it is the one of the most popular motion in sports activities. A 3 dimensional CG data for the high jump bar clear phase was used to calculate the initial velocity vector of the CG. Linear regression function and other functions such as cubic spline and derivative of Mathcad were used to calculate this vector. Finally, the approach angle to the bar and peak jump height was calculated. Programming in Mathcad was relatively easy compare to traditional computer language such as Fortran and C, because of the unique documentation method of Mathcad. Additionally the 2 and 3 dimensional graph function was very easy and useful to describe the mechanical data. If the use of Mathcad program is more popular in the field of sport biomechanics, it could greatly contribute to overcome the limit of research caused by the lack of proper programming ability.

• 응용통계연구
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• 제18권3호
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• pp.543-553
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• 2005

선형 로지스틱 모형은 신용위험 관리를 위한 신용평점 모형 구축에 있어서 널리 쓰이고 있는 방법론이다. 본 논문에서는 신용평점화를 위하여 로지스틱 회귀 방법에 기초한 스플라인 방법론을 다루고자 한다. 선형 스플라인과 자동적인 변수선택 방법을 채택하였다. 모의 실험을 통하여 스플라인 방법의 성능을 규명하였다.

• 응용통계연구
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• 제5권2호
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• pp.181-192
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• 1992

본 논문에서는 텐서 스플라인을 이용하여, 일반화된 선형모형의 회귀합수를 자료에만 의존 하는 방식으로 추정하는 문제를 고려하였다. 최우 추정법을 이용하여 회귀 함수를 추정하는 데, 이용된 텐서 스틀라인은 접목점의 수가 유한개이며, 독립변수 영역의 주변에서는 선형으 로 제한되었다. 접목점을 자료의 각 좌표의 순서 통계량에 위치하도록 했고 그 수는 AIC의 변형된 식을 최소로 하는 수로 결정 했다. 모의 실험 예를 통하여 추정량을 예시하였다.

• International Journal of Aeronautical and Space Sciences
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• 제17권2호
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• pp.240-252
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• 2016

In this paper, an on-board orbit propagator and compressing trajectory method based on B-spline for a lunar explorer are proposed. An explorer should recognize its own orbit for a successful mission operation. Generally, orbit determination is periodically performed at the ground station, and the computed orbit information is subsequently uploaded to the explorer, which would generate a heavy workload for the ground station and the explorer. A high-performance computer at the ground station is employed to determine the orbit required for the explorer in the parking orbit of Earth. The method not only reduces the workload of the ground station and the explorer, but also increases the orbital prediction accuracy. Then, the data was compressed into coefficients within a given tolerance using B-spline. The compressed data is then transmitted to the explorer efficiently. The data compression is maximized using the proposed methods. The methods are compared with a fifth order polynomial regression method. The results show that the proposed method has the potential for expansion to various deep space probes.

• Communications for Statistical Applications and Methods
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• 제17권4호
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• pp.551-560
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• 2010

This paper considers a Bayesian approach to modeling a flexible regression function under structural measurement error model. The regression function is modeled based on semiparametric regression with penalized splines. Model fitting and parameter estimation are carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Their performances are compared with those of the estimators under structural measurement error model without a semiparametric component.

• Journal of the Korean Data and Information Science Society
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• 제21권2호
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• pp.379-385
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• 2010

This paper considers Bayesian approach to modeling a flexible regression function under functional measurement error model. The regression function is modeled based on semiparametric regression with penalized splines. Model fitting and parameter estimation are carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Their performances are compared with those of the estimators under functional measurement error model without semiparametric component.