• Title/Summary/Keyword: statistics based method

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Estimation for the scale parameter of Weibull Distribution Based on Multiply Censored Samples

  • Han, Jun-Tae;Kang, Suk-Bok;Lee, Hwa-Jung
    • 한국데이터정보과학회:학술대회논문집
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    • 2004.04a
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    • pp.83-90
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    • 2004
  • We consider the problem of estimating the scale parameter of the Weibull distribution based on multiply Type-II censord samples. We propose some estimators by using the approximate maximum likelihood estimation method. The proposed estimators are compared in the sense of the mean squared error.

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Spatial Contrast Enhancement using Local Statistics based on Genetic Algorithm

  • Choo, MoonWon
    • Journal of Multimedia Information System
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    • v.4 no.2
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    • pp.89-92
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    • 2017
  • This paper investigates simple gray level image enhancement technique based on Genetic Algorithms and Local Statistics. The task of GA is to adapt the parameters of local sliding masks over pixels, finding out the best parameters preserving the brightness and possibly preventing the creation of intensity artifacts in the local area of images. The algorithm is controlled by GA as to enhance the contrast and details in the images automatically according to an object fitness criterion. Results obtained in terms of subjective and objective evaluations, show the plausibility of the method suggested here.

Estimation of Weibull Scale Parameter Based on Multiply Type-II Censored Samples

  • Kang, Suk-Bok;Lee, Hwa-Jung;Han, Jun-Tae
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.3
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    • pp.593-603
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    • 2004
  • We consider the problem of estimating the scale parameter of the Weibull distribution based on multiply Type-II censored samples. We propose two estimators by using the approximate maximum likelihood estimation method for Weibull and extreme value distributions. The proposed estimators are compared in the sense of the mean squared error.

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Adaptive Exponential Smoothing Method Based on Structural Change Statistics (구조변화 통계량을 이용한 적응적 지수평활법)

  • Kim, Jeong-Il;Park, Dae-Geun;Jeon, Deok-Bin;Cha, Gyeong-Cheon
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2006.11a
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    • pp.165-168
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    • 2006
  • Exponential smoothing methods do not adapt well to unexpected changes in underlying process. Over the past few decades a number of adaptive smoothing models have been proposed which allow for the continuous adjustment of the smoothing constant value in order to provide a much earlier detection of unexpected changes. However, most of previous studies presented ad hoc procedure of adaptive forecasting without any theoretical background. In this paper, we propose a detection-adaptation procedure applied to simple and Holt's linear method. We derive level and slope change detection statistics based on Bayesian statistical theory and present distribution of the statistics by simulation method. The proposed procedure is compared with previous adaptive forecasting models using simulated data and economic time series data.

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A Comparative Study on the Performance of Bayesian Partially Linear Models

  • Woo, Yoonsung;Choi, Taeryon;Kim, Wooseok
    • Communications for Statistical Applications and Methods
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    • v.19 no.6
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    • pp.885-898
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    • 2012
  • In this paper, we consider Bayesian approaches to partially linear models, in which a regression function is represented by a semiparametric additive form of a parametric linear regression function and a nonparametric regression function. We make a comparative study on the performance of widely used Bayesian partially linear models in terms of empirical analysis. Specifically, we deal with three Bayesian methods to estimate the nonparametric regression function, one method using Fourier series representation, the other method based on Gaussian process regression approach, and the third method based on the smoothness of the function and differencing. We compare the numerical performance of three methods by the root mean squared error(RMSE). For empirical analysis, we consider synthetic data with simulation studies and real data application by fitting each of them with three Bayesian methods and comparing the RMSEs.

New approach for analysis of progressive Type-II censored data from the Pareto distribution

  • Seo, Jung-In;Kang, Suk-Bok;Kim, Ho-Yong
    • Communications for Statistical Applications and Methods
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    • v.25 no.5
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    • pp.569-575
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    • 2018
  • Pareto distribution is important to analyze data in actuarial sciences, reliability, finance, and climatology. In general, unknown parameters of the Pareto distribution are estimated based on the maximum likelihood method that may yield inadequate inference results for small sample sizes and high percent censored data. In this paper, a new approach based on the regression framework is proposed to estimate unknown parameters of the Pareto distribution under the progressive Type-II censoring scheme. The proposed method provides a new regression type estimator that employs the spacings of exponential progressive Type-II censored samples. In addition, the provided estimator is a consistent estimator with superior performance compared to maximum likelihood estimators in terms of the mean squared error and bias. The validity of the proposed method is assessed through Monte Carlo simulations and real data analysis.

Inference of Gene Regulatory Networks via Boolean Networks Using Regression Coefficients

  • Kim, Ha-Seong;Choi, Ho-Sik;Lee, Jae-K.;Park, Tae-Sung
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • 2005.09a
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    • pp.339-343
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    • 2005
  • Boolean networks(BN) construction is one of the commonly used methods for building gene networks from time series microarray data. However, BN has two major drawbacks. First, it requires heavy computing times. Second, the binary transformation of the microarray data may cause a loss of information. This paper propose two methods using liner regression to construct gene regulatory networks. The first proposed method uses regression based BN variable selection method, which reduces the computing time significantly in the BN construction. The second method is the regression based network method that can flexibly incorporate the interaction of the genes using continuous gene expression data. We construct the network structure from the simulated data to compare the computing times between Boolean networks and the proposed method. The regression based network method is evaluated using a microarray data of cell cycle in Caulobacter crescentus.

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Long-term Fading Statistics-Based Power Allocation for Fixed Decode-and-Forward Relays

  • Kong, Hyung-Yun;Bao, Vo Nguyen Quae
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.33 no.12A
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    • pp.1138-1146
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    • 2008
  • This paper considers 2-hop wireless cooperative communications networks with fixed decode-and-forward relays. Specifically, we first derive the closed-form BER expression for theoretically evaluating the end-to-end performance of these networks. Then, based on this expression and long-tenn fading statistics, we propose a power allocation method for source and relay. Such a method brings about multiple advantages in tenn of spectral efficiency and implementation complexity over other power allocation methods based on instantaneous fading statistics. A variety of numerical results reveal that the cooperative communications scheme with the proposed power allocation significantly outperforms that with the equal power allocation and the direct transmission scheme for any position of the relay subject to the same total transmit power constraint.

Blind Source Separation via Principal Component Analysis

  • Choi, Seung-Jin
    • Journal of KIEE
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    • v.11 no.1
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    • pp.1-7
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    • 2001
  • Various methods for blind source separation (BSS) are based on independent component analysis (ICA) which can be viewed as a nonlinear extension of principal component analysis (PCA). Most existing ICA methods require certain nonlinear functions (which leads to higher-order statistics) depending on the probability distributions of sources, whereas PCA is a linear learning method based on second-order statistics. In this paper we show that the PCA can be applied to the task of BBS, provided that source are spatially uncorrelated but temporally correlated. Since the resulting method is based on only second-order statistics, it avoids the nonlinear function and is able to separate mixtures of several colored Gaussian sources, in contrast to the conventional ICA methods.

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Novel estimation based on a minimum distance under the progressive Type-II censoring scheme

  • Young Eun Jeon;Suk-Bok Kang;Jung-In Seo
    • Communications for Statistical Applications and Methods
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    • v.30 no.4
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    • pp.411-421
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    • 2023
  • This paper provides a new estimation equation based on the concept of a minimum distance between the empirical and theoretical distribution functions under the most widely used progressive Type-II censoring scheme. For illustrative purposes, simulated and real datasets from a three-parameter Weibull distribution are analyzed. For comparison, the most popular estimation methods, the maximum likelihood and maximum product of spacings estimation methods, are developed together. In the analysis of simulated datasets, the excellence of the provided estimation method is demonstrated through the degree of the estimation failure of the likelihood-based method, and its validity is demonstrated through the mean squared errors and biases of the estimators obtained from the provided estimation equation. In the analysis of the real dataset, two types of goodness-of-fit tests are performed on whether the observed dataset has the three-parameter Weibull distribution under the progressive Type-II censoring scheme, through which the performance of the new estimation equation provided is examined.