• 제목/요약/키워드: Statistical distributions

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Rank Scores for Linear Models under Asymmetric Distributions

  • Choi, Young-Hun
    • Communications for Statistical Applications and Methods
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    • 제13권2호
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    • pp.359-368
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    • 2006
  • In this paper we derived the asymptotic relative efficiency, ARE(ms, rs), of our new score function with respect to the McKean and Sievers scores for the asymmetric error distributions which often occur in practice. We thoroughly explored the asymptotic relative efficiency, ARE(ms, rs), of our score function that provides much improvement over the McKean and Sievers scores for all values of r and s under asymmetric distributions.

Sharp Expectation Bounds on Extreme Order Statistics from Possibly Dependent Random Variables

  • Yun, Seokhoon
    • Communications for Statistical Applications and Methods
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    • 제11권3호
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    • pp.455-463
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    • 2004
  • In this paper, we derive sharp upper and lower expectation bounds on the extreme order statistics from possibly dependent random variables whose marginal distributions are only known. The marginal distributions of the considered random variables may not be the same and the expectation bounds are completely determined by the marginal distributions only.

A Family of Extended NQD Bivariate Distributions with Continuous Marginals

  • Ryu, Dae-Hee
    • Communications for Statistical Applications and Methods
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    • 제19권1호
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    • pp.85-95
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    • 2012
  • In this paper we define extended negative quadrant dependence which is weaker negative quadrant dependence and show conditions for having extended negative quadrant dependence property. We also derive generalized Farlie-Gumbel-Morgenstern uniform distributions that possess the extended quadrant dependence property.

Asymptotic Relative Efficiencies of Chaudhuri′s Estimators for the Multivariate One Sample Location Problem

  • Park, Kyungmee
    • Communications for Statistical Applications and Methods
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    • 제8권3호
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    • pp.875-883
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    • 2001
  • We derive the asymptotic relative efficiencies in two special cases of Chaudhuri's estimators for the multivariate one sample problem. And we compare those two when observations are independent and identically distributed from a family of spherically symmetric distributions including normal distributions.

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A Family of Truncated Skew-Normal Distributions

  • Kim, Hea-Jung
    • Communications for Statistical Applications and Methods
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    • 제11권2호
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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.

Estimating survival distributions for two-stage adaptive treatment strategies: A simulation study

  • Vilakati, Sifiso;Cortese, Giuliana;Dlamini, Thembelihle
    • Communications for Statistical Applications and Methods
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    • 제28권5호
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    • pp.411-424
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    • 2021
  • Inference following two-stage adaptive designs (also known as two-stage randomization designs) with survival endpoints usually focuses on estimating and comparing survival distributions for the different treatment strategies. The aim is to identify the treatment strategy(ies) that leads to better survival of the patients. The objectives of this study were to assess the performance three commonly cited methods for estimating survival distributions in two-stage randomization designs. We review three non-parametric methods for estimating survival distributions in two-stage adaptive designs and compare their performance using simulation studies. The simulation studies show that the method based on the marginal mean model is badly affected by high censoring rates and response rate. The other two methods which are natural extensions of the Nelson-Aalen estimator and the Kaplan-Meier estimator have similar performance. These two methods yield survival estimates which have less bias and more precise than the marginal mean model even in cases of small sample sizes. The weighted versions of the Nelson-Aalen and the Kaplan-Meier estimators are less affected by high censoring rates and low response rates. The bias of the method based on the marginal mean model increases rapidly with increase in censoring rate compared to the other two methods. We apply the three methods to a leukemia clinical trial dataset and also compare the results.

A new extended alpha power transformed family of distributions: properties, characterizations and an application to a data set in the insurance sciences

  • Ahmad, Zubair;Mahmoudi, Eisa;Hamedani, G.G.
    • Communications for Statistical Applications and Methods
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    • 제28권1호
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    • pp.1-19
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    • 2021
  • Heavy tailed distributions are useful for modeling actuarial and financial risk management problems. Actuaries often search for finding distributions that provide the best fit to heavy tailed data sets. In the present work, we introduce a new class of heavy tailed distributions of a special sub-model of the proposed family, called a new extended alpha power transformed Weibull distribution, useful for modeling heavy tailed data sets. Mathematical properties along with certain characterizations of the proposed distribution are presented. Maximum likelihood estimates of the model parameters are obtained. A simulation study is provided to evaluate the performance of the maximum likelihood estimators. Actuarial measures such as Value at Risk and Tail Value at Risk are also calculated. Further, a simulation study based on the actuarial measures is done. Finally, an application of the proposed model to a heavy tailed data set is presented. The proposed distribution is compared with some well-known (i) two-parameter models, (ii) three-parameter models and (iii) four-parameter models.