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Barry C. Arnold - One of the best experts on this subject based on the ideXlab platform.

  • Parameter estimation under generalized ranked set sampling
    Statistics & Probability Letters, 1999
    Co-Authors: Yonghee Kim, Barry C. Arnold
    Abstract:

    Abstract This article is concerned wIth parameter estimation under ranked set sampling (RSS) and under a generalized version of RSS (i.e., in the Ith Sample, Xri:ni is observed instead of Xi:n, so that the data are X =(X r 1 :n 1 ,…,X r k :n k )) . Bayesian parameter estimation of specified parameters under both balanced and generalized RSS is accomplished using the Gibbs Sampler.

  • Estimation of a distribution function under generalized ranked set sampling
    Communications in Statistics - Simulation and Computation, 1999
    Co-Authors: Yonghee Kim, Barry C. Arnold
    Abstract:

    This research is concerned wIth distribution estimation under RankedSet Sampling (RSS) and under a generalized version of RSS (i.e., in the Ith Sample, is observed instead of so that the data are . Distribution estimation under both balanced and generalized RSS is accomplished. Starting wIth a Dirichlet process prior for F, the common distribution of the X's, the missing X values are generated. The estimate of the distribution function F is updated (the posterior distribution is again a Dirichlet process) based on the complete pseudo data . These two steps are repeated until the estimate of F stabilizes.

Yonghee Kim - One of the best experts on this subject based on the ideXlab platform.

  • Parameter estimation under generalized ranked set sampling
    Statistics & Probability Letters, 1999
    Co-Authors: Yonghee Kim, Barry C. Arnold
    Abstract:

    Abstract This article is concerned wIth parameter estimation under ranked set sampling (RSS) and under a generalized version of RSS (i.e., in the Ith Sample, Xri:ni is observed instead of Xi:n, so that the data are X =(X r 1 :n 1 ,…,X r k :n k )) . Bayesian parameter estimation of specified parameters under both balanced and generalized RSS is accomplished using the Gibbs Sampler.

  • Estimation of a distribution function under generalized ranked set sampling
    Communications in Statistics - Simulation and Computation, 1999
    Co-Authors: Yonghee Kim, Barry C. Arnold
    Abstract:

    This research is concerned wIth distribution estimation under RankedSet Sampling (RSS) and under a generalized version of RSS (i.e., in the Ith Sample, is observed instead of so that the data are . Distribution estimation under both balanced and generalized RSS is accomplished. Starting wIth a Dirichlet process prior for F, the common distribution of the X's, the missing X values are generated. The estimate of the distribution function F is updated (the posterior distribution is again a Dirichlet process) based on the complete pseudo data . These two steps are repeated until the estimate of F stabilizes.

Chien-tai Lin - One of the best experts on this subject based on the ideXlab platform.

  • Exact inference and prediction for k-Sample exponential case under type-ii censoring
    Journal of Statistical Computation and Simulation, 2005
    Co-Authors: Narayanaswamy Balakrishnan, Chien-tai Lin
    Abstract:

    The exact inference and prediction intervals for the K-Sample exponential scale parameter under doubly Type-II censored Samples are derived using an algorIthm of Huffer and Lin [Huffer, F.W. and Lin, C.T., 2001, Computing the joint distribution of general linear combinations of spacings or exponen-tial variates. Statistica Sinica, 11, 1141–1157.]. This approach provides a simple way to determine the exact percentage points of the pivotal quantity based on the best linear unbiased estimator in order to develop exact inference for the scale parameter as well as to construct exact prediction intervals for failure times unobserved in the Ith Sample. Similarly, exact prediction intervals for failure times of units from a future Sample can also be easily obtained.

  • Exact inference and prediction for K-Sample two-parameter exponential case under general Type-II censoring
    Journal of Statistical Computation and Simulation, 2004
    Co-Authors: Narayanaswamy Balakrishnan, Chien-tai Lin, Ping Shing Chan
    Abstract:

    Exact inference for the location and scale parameters as well as prediction intervals for K-Sample exponential case under general Type-II censored Samples are derived using an algorIthm of Huffer and Lin [Huffer, F. W. and Lin, C. T. (2001). Computing the joint distribution of general linear combinations of spacings or exponential variates. Stat. Sin., 11, 1141–1157.]. This approach provides a simple way to determine the exact percentage points of the pivotal quantities based on the best linear unbiased estimators in order to develop exact inference for the location and scale parameters as well as to construct exact prediction intervals for failure times unobserved in the Ith Sample. Similarly, exact prediction intervals for failure times of units from a future Sample can also be easily obtained. A comparison is then made wIth the approximate inference based on the maximum likelihood estimators. Finally, we present an example to illustrate all the methods of inference developed in this paper.

Narayanaswamy Balakrishnan - One of the best experts on this subject based on the ideXlab platform.

  • Exact inference and prediction for k-Sample exponential case under type-ii censoring
    Journal of Statistical Computation and Simulation, 2005
    Co-Authors: Narayanaswamy Balakrishnan, Chien-tai Lin
    Abstract:

    The exact inference and prediction intervals for the K-Sample exponential scale parameter under doubly Type-II censored Samples are derived using an algorIthm of Huffer and Lin [Huffer, F.W. and Lin, C.T., 2001, Computing the joint distribution of general linear combinations of spacings or exponen-tial variates. Statistica Sinica, 11, 1141–1157.]. This approach provides a simple way to determine the exact percentage points of the pivotal quantity based on the best linear unbiased estimator in order to develop exact inference for the scale parameter as well as to construct exact prediction intervals for failure times unobserved in the Ith Sample. Similarly, exact prediction intervals for failure times of units from a future Sample can also be easily obtained.

  • Exact inference and prediction for K-Sample two-parameter exponential case under general Type-II censoring
    Journal of Statistical Computation and Simulation, 2004
    Co-Authors: Narayanaswamy Balakrishnan, Chien-tai Lin, Ping Shing Chan
    Abstract:

    Exact inference for the location and scale parameters as well as prediction intervals for K-Sample exponential case under general Type-II censored Samples are derived using an algorIthm of Huffer and Lin [Huffer, F. W. and Lin, C. T. (2001). Computing the joint distribution of general linear combinations of spacings or exponential variates. Stat. Sin., 11, 1141–1157.]. This approach provides a simple way to determine the exact percentage points of the pivotal quantities based on the best linear unbiased estimators in order to develop exact inference for the location and scale parameters as well as to construct exact prediction intervals for failure times unobserved in the Ith Sample. Similarly, exact prediction intervals for failure times of units from a future Sample can also be easily obtained. A comparison is then made wIth the approximate inference based on the maximum likelihood estimators. Finally, we present an example to illustrate all the methods of inference developed in this paper.

D. Mcdonald - One of the best experts on this subject based on the ideXlab platform.

  • A nonparametric test for homogeneity: applications to parameter estimation
    Institute of Mathematical Statistics Lecture Notes - Monograph Series, 1994
    Co-Authors: K. Ghoudi, D. Mcdonald
    Abstract:

    Testing for homogeneity has many applications in statistical analysis. For example, regression analysis may be viewed as determining the set of parameters that makes the residuals homogeneous. Assume for each i = 1, -.-, q a small Sample n ( i ) observations is collected. Let N = x : = l n ( i ) , let F' be the empirical distribution function of the Ith Sample and let the empirical distribution function of all the Samples taken together be F . The problem is to test if these Samples are homogeneous. Lehmann (1951) considered the problem of testing the equality of the distributions of q Samples. He proposed the statistic

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