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This paper improves the convergence
of two variance parameters in He and Sun (2000) by applying the
ancillarity-sufficiency interweaving strategy (ASIS in Yu and Meng
(2011)) algorithm to the Gibbs sampling steps. The performance of
the ASIS algorithm is compared with the regular Gibbs sampling by
the potential scale reduction factor, trace plots and posterior
estimates. The convergence of one parameter improves greatly, but
the other one does not have a very significant improvement. However,
the overall sampling performance has improved greatly since it needs
much fewer iterations than using regular Gibbs sampling to achieve
convergence.
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We express the group structure of
sets of a copula , survival copula
, the dual of a
copula , and the co-copula
, give the best-possible
bounds for the last three functions where the value of every
function is known at a single point. Especially, we find an general
narrowing effectiveness coefficient for
,
compare the values of where
to
thoses for being other values in
.
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Cardy gave a probabilistic estimation
for the critical percolation clusters crossing a rectangle without
touching the upper and lower boundaries of the rectangle. Lawler,
Schramm and Werner obtained similar probabilistic estimation for the
chordal stochastic Loewner evolution with parameter 6
crossing a rectangle. In this paper, we generalize
the latter result to the case .
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In this paper, some new exact tests and
confidence intervals of variance components in nested error
component regression model with three random effects are developed
by using generalized p-value and generalized confidence interval.
Invariance of these tests and confidence intervals under scale
transformation is also discussed. It is showed that the generalized
p-value is feasible and effective to resolve the hypothesis
testing problems with nuisance parameters. A simulation study is
conducted to illustrate the powers of these tests and coverage
probabilities.
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In this paper, we consider two linear models with
missing data, where the covariates are not missing, but response variables are missing
at random(MAR). The inverse probability weighted imputation is used to impute the missing
data of response variables, we can obtain the 'complete' data for two linear regression models.
Then we can construct the empirical log-likelihood ratios of quantile differences of response
variables. And the difference is that the asymptotic distributions for the empirical
log-likelihood ratios of quantile differences of response variables are standard comparing with the results of previous studies. The empirical likelihood confidence
intervals for quantile difference of response variables is more accurate because the errors
caused right of the coefficient estimates is reduced.
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Reference analysis was first introduced by
Bernardo (1979) and further developed by Berger and Bernardo (1992a).
Berger et al.(2001) proposed a new method to obtain exact reference
priors, which is proved to be one of the most successful methods to
derive noninformative prior distributions. In this paper, the algorithm
proposed in Berger et al.(2001) is used to obtain reference priors
for the growth curve model with general covariance structures.
Simultaneously, some applications of the corresponding results are
presented.
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This essay introduces cohort mortality
dependence in Lee-Carter modeling to illustrate the dynamic changes
of mortality. Using the longevity bond designation of Lin and Cox
(2005) and on the basis of Chinese mortality experience, we analyze
the pricing result of longevity bond in multivariate Wang risk measure.
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This paper presents a systematic overview
of the genome-wide association study. We mainly focus on the
statistical methods. Some problems and challenges are also
provided.
