• Title/Summary/Keyword: family of distributions

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Exponential family of circular distributions

  • Kim, Sung-Su
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.6
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    • pp.1217-1222
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    • 2011
  • In this paper, we show that any circular density can be closely approximated by an exponential family of distributions. Therefore we propose an exponential family of distributions as a new family of circular distributions, which is absolutely suitable to model any shape of circular distributions. In this family of circular distributions, the trigonometric moments are found to be the uniformly minimum variance unbiased estimators (UMVUEs) of the parameters of distribution. Simulation result and goodness of fit test using an asymmetric real data set show usefulness of the novel circular distribution.

A Projected Exponential Family for Modeling Semicircular Data

  • Kim, Hyoung-Moon
    • The Korean Journal of Applied Statistics
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    • v.23 no.6
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    • pp.1125-1145
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    • 2010
  • For modeling(skewed) semicircular data, we derive a new exponential family of distributions. We extend it to the l-axial exponential family of distributions by a projection for modeling any arc of arbitrary length. It is straightforward to generate samples from the l-axial exponential family of distributions. Asymptotic result reveals that the linear exponential family of distributions can be used to approximate the l-axial exponential family of distributions. Some trigonometric moments are also derived in closed forms. The maximum likelihood estimation is adopted to estimate model parameters. Some hypotheses tests and confidence intervals are also developed. The Kolmogorov-Smirnov test is adopted for a goodness of t test of the l-axial exponential family of distributions. Samples of orientations are used to demonstrate the proposed model.

A NEW FAMILY OF NEGATIVE QUADRANT DEPENDENT BIVARIATE DISTRIBUTIONS WITH CONTINUOUS MARGINALS

  • Han, Kwang-Hee
    • Journal of the Chungcheong Mathematical Society
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    • v.24 no.4
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    • pp.795-805
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    • 2011
  • In this paper, we study a family of continuous bivariate distributions that possesses the negative quadrant dependence property and the generalized negatively quadrant dependent F-G-M copula. We also develop the partial ordering of this new parametric family of negative quadrant dependent distributions.

On a Skew-t Distribution

  • Kim, Hea-Jung
    • Communications for Statistical Applications and Methods
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    • v.8 no.3
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    • pp.867-873
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    • 2001
  • In this paper we propose a family of skew- f distributions. The family is derived by a scale mixtures of skew-normal distributions introduced by Azzalini (1985) and Henze (1986). The salient features of the family are mathematical tractability and strict inclusion of the normal law. Further it includes a shape parameter, to some extent, controls the index of skewness. Necessary theory involved in deriving the family of distributions is provided and main properties of the family are also studied.

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THE LOGARITHMIC KUMARASWAMY FAMILY OF DISTRIBUTIONS: PROPERTIES AND APPLICATIONS

  • Ahmad, Zubair
    • Communications of the Korean Mathematical Society
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    • v.34 no.4
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    • pp.1335-1352
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    • 2019
  • In this article, a new family of lifetime distributions by adding two additional parameters is introduced. The new family is called, the logarithmic Kumaraswamy family of distributions. For the proposed family, explicit expressions for some mathematical properties are derived. Maximum likelihood estimates of the model parameters are also obtained. This method is applied to develop a new lifetime model, called the logarithmic Kumaraswamy Weibull distribution. The proposed model is very flexible and capable of modeling data with increasing, decreasing, unimodal or modified unimodal shaped hazard rates. To access the behavior of the model parameters, a simulation study has been carried out. Finally, the potentiality of the new method is proved via analyzing two real data sets.

Relationships between Distribution of Number of Transferable Embryos and Inbreeding Coefficient in a MOET Dairy Cattle Population

  • Terawaki, Y.;Asada, Y.
    • Asian-Australasian Journal of Animal Sciences
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    • v.15 no.12
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    • pp.1686-1689
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    • 2002
  • Genetic gains and inbreeding coefficients in a Holstein MOET breeding population were predicted under different conditions relating to the distribution of the number of transferable embryos collected per flush using Monte Carlo simulation. The numbers of transferable embryos collected per flush were determined using five distributions (distributions 1, 3, 5, 7 and 9) with different aspects and similar means. Distributions 1, 3, 5, 7 and 9 were assumed to have gamma distribution's parameters ($\alpha$ and $\beta$) of (1 and 4.4), (3 and 1.47), (5 and 0.88), (7 and 0.63) and (9 and 0.49), respectively. Inbreeding rates were statistically significantly different among distributions but genetic gains were not. Relationships between inbreeding rates and variances of family size could be were clearly distinguished. The highest inbreeding coefficients were predicted in distribution 1 with the largest variance of family size, while distributions 5, 7 and 9 with smaller variance of family size had lower inbreeding coefficients.

A Family of Truncated Skew-Normal Distributions

  • Kim, Hea-Jung
    • Communications for Statistical Applications and Methods
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    • v.11 no.2
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    • pp.265-274
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    • 2004
  • The paper extends earlier work on the skew-normal distribution, a family of distributions including normal, but with extra parameter to regulate skewness. The present work introduces a singly truncated parametric family that strictly includes a truncated normal distribution, and studies its properties, with special emphasis on the relation with bivariate normal distribution.

Projected Circular and l-Axial Skew-Normal Distributions

  • Seo, Han-Son;Shin, Jong-Kyun;Kim, Hyoung-Moon
    • The Korean Journal of Applied Statistics
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    • v.22 no.4
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    • pp.879-891
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    • 2009
  • We developed the projected l-axial skew-normal(LASN) family of distributions for I-axial data. The LASN family of distributions contains the semicircular skew-normal(SCSN) and the circular skew-normal(CSN) families of distributions as special cases. The LASN densities are similar to the wrapped skew-normal densities for the small values of the scale parameter. However CSN densities have more heavy tails than those of the wrapped skew-normal densities on the circle. Furthermore the CSN densities have two modes as the scale parameter increases. The LASN distribution has very convenient mathematical features. We extend the LASN family of distributions to a bivariate case.

Comparative Analysis on the Design Rainfall derived by Gamma Family Distributions (Gamma Family군의 분포형에 의한 강우의 빈도분석)

  • Ryoo, Kyong-Sik;Lee, Soon-Hyuk;Maeng, Sung-Jin;Song, Ki-Hurn;Kim, Gi-Chang
    • Proceedings of the Korean Society of Agricultural Engineers Conference
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    • 2003.10a
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    • pp.439-442
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    • 2003
  • This study was conducted to choose optimal distribution and to estimate properly parameters for the derivation of design rainfall in Gamma Family. Design rainfall derived by Gamma Family Distributions were compared by the Relative Mean Errors(RME) and Relative Absolute Errors(RAE) for the consecutive durations of 1, 3, 6, 12, 24, 36, 48 and 72hr and 65 regions all over the regions except Cheju and Wulreung islands in Korea. Consequently, Design rainfall derived by Indirect Method of Moments in the Log-Pearson Type 3 distribution are seemed to be more reasonable than those of other distributions in Gamma Family.

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CHARACTERIZATION OF CONTINUOUS DISTRIBUTIONS THROUGH RECORD STATISTICS

  • Khan, Abdul Hamid;Faizan, Mohd.;Haque, Ziaul
    • Communications of the Korean Mathematical Society
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    • v.25 no.3
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    • pp.485-489
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    • 2010
  • A family of continuous probability distribution has been characterized through the difference of two conditional expectations, conditioned on a non-adjacent record statistic. Also, a result based on the unconditional expectation and a conditional expectation is used to characterize a family of distributions. Further, some of its deductions are also discussed.