• Title/Summary/Keyword: Distribution Department

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Noninformative priors for the common location parameter in half-normal distributions

  • Kang, Sang-Gil;Kim, Dal-Ho;Lee, Woo-Dong
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.4
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    • pp.757-764
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    • 2010
  • In this paper, we develop the reference priors for the common location parameter in the half-normal distributions with unequal scale paramters. We derive the reference priors as noninformative prior and prove the propriety of joint posterior distribution under the general prior including the reference priors. Through the simulation study, we show that the proposed reference priors match the target coverage probabilities in a frequentist sense.

Marginal distribution of crossing time and renewal numbers related with two-state Erlang process

  • Talpur, Mir Ghulam Hyder;Zamir, Iffat;Ali, M. Masoom
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.1
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    • pp.191-202
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    • 2009
  • In this study, we drive the one dimensional marginal transform function, probability density function and probability distribution function for the random variables $T_{{\xi}N}$ (Time taken by the servers during the vacations), ${\xi}_N$(Number of vacations taken by the servers) and ${\eta}_N$(Number of customers or units arrive in the system) by controlling the variability of two random variables simultaneously.

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Approximate MLE for Rayleigh Distribution in Singly Right Censored Samples

  • Jungsoo Woo;Suk-Bok Kang;Young-Suk Cho;Sangchoon Jeon
    • Communications for Statistical Applications and Methods
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    • v.5 no.1
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    • pp.225-230
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    • 1998
  • By assuming a singly right cenosred sample, we propose the approximate maximum likelihood estimator (AMLE) of the scale parameter of the p-dimensional Rayleigh distribution. We compare the proposed estimator in ·terms of the mean squared error through Monte Carlo methods.

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ESTIMATING THE SIMULTANEOUS CONFIDENCE LEVELS FOR THE DIFFERENCE OF PROPORTIONS FROM MULTIVARIATE BINOMIAL DISTRIBUTIONS

  • Jeong, Hyeong-Chul;Jhun, Myoung-Shic;Lee, Jae-Won
    • Journal of the Korean Statistical Society
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    • v.36 no.3
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    • pp.397-410
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    • 2007
  • For the two groups data from multivariate binomial distribution, we consider a bootstrap approach to inferring the simultaneous confidence level and its standard error of a collection of the dependent confidence intervals for the difference of proportions with an experimentwise error rate at the a level are presented. The bootstrap method is used to estimate the simultaneous confidence probability for the difference of proportions.

STUDY ON MALOCCLUSION DISTRIBUTION IN ORTHODONTIC DEPARTMENT (부정교합 환자의 내원상황에 관한 연구)

  • Seo, Jeong-Hun
    • The Journal of the Korean dental association
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    • v.19 no.12 s.151
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    • pp.1027-1030
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    • 1981
  • 2065 patients who visited orthodontic department form 1977. 7. 16. to 1981. 9. 30. were surveyed on the yearly tendency of patient distribution and the state of Angle's Classification. The results were as follows: 1. There was increased visiting rate of patient per year except the year 1980. 2. 8-13 age group was 55% in total visiting patient and 20 age over group was 11.0%. 3. Class I malocclusion was 42.3% in total visiting patient, more Class III malocclusion was prevalent than Class II malocclusion.

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ON THE VALUE DISTRIBUTION OF DIFFERENTIAL POLYNOMIALS

  • Bhoosnurmath, Subhas S.;Kulkarni, Milind Narayanrao;Yu, Kit-Wing
    • Bulletin of the Korean Mathematical Society
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    • v.45 no.3
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    • pp.427-435
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    • 2008
  • In this paper we consider the problem of whether certain homogeneous or non-homogeneous differential polynomials in f(z) necessarily have infinitely many zeros. Particularly, this extends a result of Gopalakrishna and Bhoosnurmath [3, Theorem 2] for a general differential polynomial of degree $\bar{d}$(P) and lower degree $\underline{d}$(P).

Immunohistochemical study on the distribution of ion channels in rat trigeminal sensory nucleus.

  • Park, H.Y.;Choi, G.W.;Choi, H.Y.
    • Proceedings of the KACD Conference
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    • 2001.11a
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    • pp.561.2-561
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    • 2001
  • Trigeminal sensory nerves relay mechanical, thermal, chemical and proprioceptive information from craniofacial region. Therefore, it is important of dentistry. Trigeminal sensory nucleus consists of principal sensory trigeminal nucleus, spinal trigeminal nuclei, mesencephalic trigeminal nucleus. Transmission of these sensation depends on function and distribution of ion channels.(omitted)

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INFERENCE FOR ABSOLUTE LORENZ CURVE AND ABSOLUTE LORENZ ORDERING

  • Arora Sangeeta;Jain Kanchan;Pundir Sudesh
    • Journal of the Korean Statistical Society
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    • v.35 no.3
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    • pp.305-316
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    • 2006
  • Absolute Lorenz curve plays an important role for measuring absolute income inequality. Properties of absolute Lorenz curve are listed. Asymptotically distribution free and consistent tests have been proposed for comparing two absolute Lorenz curves in the whole interval [P1, P2] where 0 < P1 < P2 < 1. Absolute Lorenz ordering has been discussed for some distributions.

ASYMPTPTIC DISTRIBUTION OF LIKELINOOD RATIO STATISTIC FOR TESTING MULTISAMPLE SPHERICITY

  • Gupta, A.K.;Nagar, D.K.;Jain, Kalpana
    • Journal of the Korean Statistical Society
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    • v.21 no.1
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    • pp.14-26
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    • 1992
  • In this paper, asymptotic expansions of the distribution of the likelihood ratio statistic for testing multisample sphericity have been derived in the null and nonnull cases when the alternatives are close to the null hypothesis. These expansions are obtained in the form of series of data distributions.

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A Bayesian Wavelet Threshold Approach for Image Denoising

  • Ahn, Yun-Kee;Park, Il-Su;Rhee, Sung-Suk
    • Communications for Statistical Applications and Methods
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    • v.8 no.1
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    • pp.109-115
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    • 2001
  • Wavelet coefficients are known to have decorrelating properties, since wavelet is orthonormal transformation. but empirically, those wavelet coefficients of images, like edges, are not statistically independent. Jansen and Bultheel(1999) developed the empirical Bayes approach to improve the classical threshold algorithm using local characterization in Markov random field. They consider the clustering of significant wavelet coefficients with uniform distribution. In this paper, we developed wavelet thresholding algorithm using Laplacian distribution which is more realistic model.

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