• Title/Summary/Keyword: log-variance

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Discontinuous log-variance function estimation with log-residuals adjusted by an estimator of jump size (점프크기추정량에 의한 수정된 로그잔차를 이용한 불연속 로그분산함수의 추정)

  • Hong, Hyeseon;Huh, Jib
    • The Korean Journal of Applied Statistics
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    • v.30 no.2
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    • pp.259-269
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    • 2017
  • Due to the nonnegativity of variance, most of nonparametric estimations of discontinuous variance function have used the Nadaraya-Watson estimation with residuals. By the modification of Chen et al. (2009) and Yu and Jones (2004), Huh (2014, 2016a) proposed the estimators of the log-variance function instead of the variance function using the local linear estimator which has no boundary effect. Huh (2016b) estimated the variance function using the adjusted squared residuals by the estimated jump size in the discontinuous variance function. In this paper, we propose an estimator of the discontinuous log-variance function using the local linear estimator with the adjusted log-squared residuals by the estimated jump size of log-variance function like Huh (2016b). The numerical work demonstrates the performance of the proposed method with simulated and real examples.

Testing of a discontinuity point in the log-variance function based on likelihood (가능도함수를 이용한 로그분산함수의 불연속점 검정)

  • Huh, Jib
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.1
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    • pp.1-9
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    • 2009
  • Let us consider that the variance function in regression model has a discontinuity/change point at unknown location. Yu and Jones (2004) proposed the local polynomial fit to estimate the log-variance function which break the positivity of the variance. Using the local polynomial fit, Huh (2008) estimate the discontinuity point of the log-variance function. We propose a test for the existence of a discontinuity point in the log-variance function with the estimated jump size in Huh (2008). The proposed method is based on the asymptotic distribution of the estimated jump size. Numerical works demonstrate the performance of the method.

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Small sample likelihood based inference for the normal variance ratio

  • Lee, Woo Dong
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.4
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    • pp.911-918
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    • 2013
  • This study deals with the small sample likelihood based inference for the ratio of two normal variances. The small sample likelihood inference is an approximation method. The signed log-likelihood ratio statistic and the modified signed log-likelihood ratio statistic, which converge to standard normal distribution, are proposed for the normal variance ratio. Through the simulation study, the coverage probabilities of confidence interval and power of the exact, the signed log-likelihood and the modified signed log-likelihood ratio statistic will be compared. A real data example will be provided.

Comparison study on kernel type estimators of discontinuous log-variance (불연속 로그분산함수의 커널추정량들의 비교 연구)

  • Huh, Jib
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.1
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    • pp.87-95
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    • 2014
  • In the regression model, Kang and Huh (2006) studied the estimation of the discontinuous variance function using the Nadaraya-Watson estimator with the squared residuals. The local linear estimator of the log-variance function, which may have the whole real number, was proposed by Huh (2013) based on the kernel weighted local-likelihood of the ${\chi}^2$-distribution. Chen et al. (2009) estimated the continuous variance function using the local linear fit with the log-squared residuals. In this paper, the estimator of the discontinuous log-variance function itself or its derivative using Chen et al. (2009)'s estimator. Numerical works investigate the performances of the estimators with simulated examples.

NOISE VARIANCE ESTIMATION OF SAR IMAGE IN LOG DOMAIN

  • Chitwong S.;Minhayenud S.;Intajag S.;Cheevasuvit F.
    • Proceedings of the KSRS Conference
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    • 2004.10a
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    • pp.574-576
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    • 2004
  • Since variance of noise is important parameter for a noise filter to reduce noise in image and the performance of noise filter is dependent on estimated variance. In this paper, we apply additive noise variance estimation method to estimate variance of speckle noise of synthetic aperture radar (SAR) imagery. Generally, speckle noise is in multiplicative model, logarithmic transformation is then used to transform multiplicative model into additive model. Here, speckle noise is generally modeled as Gamma distribution function with different looks. The additive noise variance estimation is processed in log domain. The synthesis image and real image of SAR are implemented to test and confirm results and show that more accurate estimation can be achieved.

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Notes on the Ratio and the Right-Tail Probability in a Log-Laplace Distribution

  • Woo, Jung-Soo
    • Journal of the Korean Data and Information Science Society
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    • v.18 no.4
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    • pp.1171-1177
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    • 2007
  • We consider estimation of the right-tail probability in a log-Laplace random variable, As we derive the density of ratio of two independent log-Laplace random variables, the k-th moment of the ratio is represented by a special mathematical function. and hence variance of the ratio can be represented by a psi-function.

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Characteristics of Cow´s Voices in Time and Frequency domains for Recognition

  • Ikeda, Yoshio;Ishii, Y.
    • Agricultural and Biosystems Engineering
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    • v.2 no.1
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    • pp.15-23
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    • 2001
  • On the assumption that the voices of the cows are produced by the linear prediction filter, we characterized the cows’voices. The order of this filter was determined by examining the voice characteristics both in time and frequency domains. The proposed order of the linear prediction filter is 15 for modeling voice production of the cow. The characteristics of the amplitude envelope of the voice signal was investigated by analyzing the sequence of the short time variance both in time and frequency domains, and the new parameters were defined. One of the coefficients o the linear prediction filter generating the voice signal, the fundamental frequency, the slope of the straight line regressed from the log-log spectra of the short time variance and the coefficients of the linear prediction filter generating the sequence of the short time variance of the voice signal can differentiate the two cows.

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AUTOMATED ELECTROFACIES DETERMINATION USING MULTIVARIATE STATISTICAL ANALYSIS

  • Kim Jungwhan;Lim Jong-Se
    • 한국석유지질학회:학술대회논문집
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    • spring
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    • pp.10-14
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    • 1998
  • A systematic methodology is developed for the electrofacies determination from wireline log data using multivariate statistical analysis. To consider corresponding contribution of each log and reduce the computational dimension, multivariate logs are transformed into a single variable through principal components analysis. Resultant principal components logs are segmented using the statistical zonation method to enhance the efficiency and quality of the interpreted results. Hierarchical cluster analysis is then used to group the segments into electrofacies. Optimal number of groups is determined on the basis of the ratio of within-group variance to total variance and core data. This technique is applied to the wells in the Korea Continental Shelf. The results of field application demonstrate that the prediction of lithology based on the electrofacies classification matches well to the core and the cutting data with high reliability This methodology for electrofacies classification can be used to define the reservoir characteristics which are helpful to the reservoir management.

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Diagnostics for Heteroscedasticity in Mixed Linear Models

  • Ahn, Chul-Hwan
    • Journal of the Korean Statistical Society
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    • v.19 no.2
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    • pp.171-175
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    • 1990
  • A diagnostic test for detecting nonconstant variance in mixed linear models based on the score statistic is derived through the technique of model expansion, and compared to the log likelihood ratio test.

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A SUPPLEMENT TO PRECISE ASYMPTOTICS IN THE LAW OF THE ITERATED LOGARITHM FOR SELF-NORMALIZED SUMS

  • Hwang, Kyo-Shin
    • Journal of the Korean Mathematical Society
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    • v.45 no.6
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    • pp.1601-1611
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    • 2008
  • Let X, $X_1$, $X_2$, ... be i.i.d. random variables with zero means, variance one, and set $S_n\;=\;{\sum}^n_{i=1}\;X_i$, $n\;{\geq}\;1$. Gut and $Sp{\check{a}}taru$ [3] established the precise asymptotics in the law of the iterated logarithm and Li, Nguyen and Rosalsky [7] generalized their result under minimal conditions. If P($|S_n|\;{\geq}\;{\varepsilon}{\sqrt{2n\;{\log}\;{\log}\;n}}$) is replaced by E{$|S_n|/{\sqrt{n}}-{\varepsilon}{\sqrt{2\;{\log}\;{\log}\;n}$}+ in their results, the new one is called the moment version of precise asymptotics in the law of the iterated logarithm. We establish such a result for self-normalized sums, when X belongs to the domain of attraction of the normal law.