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991.
Order statistics from trivariate normal and -distributions in terms of generalized skew-normal and skew- distributions 总被引:1,自引:0,他引:1
We consider here a generalization of the skew-normal distribution, GSN(λ1,λ2,ρ), defined through a standard bivariate normal distribution with correlation ρ, which is a special case of the unified multivariate skew-normal distribution studied recently by Arellano-Valle and Azzalini [2006. On the unification of families of skew-normal distributions. Scand. J. Statist. 33, 561–574]. We then present some simple and useful properties of this distribution and also derive its moment generating function in an explicit form. Next, we show that distributions of order statistics from the trivariate normal distribution are mixtures of these generalized skew-normal distributions; thence, using the established properties of the generalized skew-normal distribution, we derive the moment generating functions of order statistics, and also present expressions for means and variances of these order statistics.Next, we introduce a generalized skew-tν distribution, which is a special case of the unified multivariate skew-elliptical distribution presented by Arellano-Valle and Azzalini [2006. On the unification of families of skew-normal distributions. Scand. J. Statist. 33, 561–574] and is in fact a three-parameter generalization of Azzalini and Capitanio's [2003. Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t distribution. J. Roy. Statist. Soc. Ser. B 65, 367–389] univariate skew-tν form. We then use the relationship between the generalized skew-normal and skew-tν distributions to discuss some properties of generalized skew-tν as well as distributions of order statistics from bivariate and trivariate tν distributions. We show that these distributions of order statistics are indeed mixtures of generalized skew-tν distributions, and then use this property to derive explicit expressions for means and variances of these order statistics. 相似文献
992.
The responses obtained from response surface designs that are run sequentially often exhibit serial correlation or time trends. The order in which the runs of the design are performed then has an impact on the precision of the parameter estimators. This article proposes the use of a variable-neighbourhood search algorithm to compute run orders that guarantee a precise estimation of the effects of the experimental factors. The importance of using good run orders is demonstrated by seeking D-optimal run orders for a central composite design in the presence of an AR(1) autocorrelation pattern. 相似文献
993.
We consider the problem of hypotheses testing with the basic simple hypothesis: observed sequence of points corresponds to stationary Poisson process with known intensity against a composite one-sided parametric alternative that this is a stress-release point process. The underlying family of measures is locally asymptotically quadratic and we describe the behavior of score-function, likelihood ratio and Wald tests in the asymptotics of large samples. The results of numerical simulations are presented. 相似文献
994.
Nonparametric density estimation in the presence of measurement error is considered. The usual kernel deconvolution estimator
seeks to account for the contamination in the data by employing a modified kernel. In this paper a new approach based on a
weighted kernel density estimator is proposed. Theoretical motivation is provided by the existence of a weight vector that
perfectly counteracts the bias in density estimation without generating an excessive increase in variance. In practice a data
driven method of weight selection is required. Our strategy is to minimize the discrepancy between a standard kernel estimate
from the contaminated data on the one hand, and the convolution of the weighted deconvolution estimate with the measurement
error density on the other hand. We consider a direct implementation of this approach, in which the weights are optimized
subject to sum and non-negativity constraints, and a regularized version in which the objective function includes a ridge-type
penalty. Numerical tests suggest that the weighted kernel estimation can lead to tangible improvements in performance over
the usual kernel deconvolution estimator. Furthermore, weighted kernel estimates are free from the problem of negative estimation
in the tails that can occur when using modified kernels. The weighted kernel approach generalizes to the case of multivariate
deconvolution density estimation in a very straightforward manner. 相似文献
995.
The reversible jump Markov chain Monte Carlo (MCMC) sampler (Green in Biometrika 82:711–732, 1995) has become an invaluable device for Bayesian practitioners. However, the primary difficulty with the sampler lies with the
efficient construction of transitions between competing models of possibly differing dimensionality and interpretation. We
propose the use of a marginal density estimator to construct between-model proposal distributions. This provides both a step
towards black-box simulation for reversible jump samplers, and a tool to examine the utility of common between-model mapping
strategies. We compare the performance of our approach to well established alternatives in both time series and mixture model
examples. 相似文献
996.
In this note we provide a counterexample which resolves conjectures about Hadamard matrices made in this journal. Beder [1998. Conjectures about Hadamard matrices. Journal of Statistical Planning and Inference 72, 7–14] conjectured that if H is a maximal m×n row-Hadamard matrix then m is a multiple of 4; and that if n is a power of 2 then every row-Hadamard matrix can be extended to a Hadamard matrix. Using binary integer programming we obtain a maximal 13×32 row-Hadamard matrix, which disproves both conjectures. Additionally for n being a multiple of 4 up to 64, we tabulate values of m for which we have found a maximal row-Hadamard matrix. Based on the tabulated results we conjecture that a m×n row-Hadamard matrix with m?n-7 can be extended to a Hadamard matrix. 相似文献
997.
Studying the right tail of a distribution, one can classify the distributions into three classes based on the extreme value index γ. The class γ>0 corresponds to Pareto-type or heavy tailed distributions, while γ<0 indicates that the underlying distribution has a finite endpoint. The Weibull-type distributions form an important subgroup within the Gumbel class with γ=0. The tail behaviour can then be specified using the Weibull tail index. Classical estimators of this index show severe bias. In this paper we present a new estimation approach based on the mean excess function, which exhibits improved bias and mean squared error. The asserted properties are supported by simulation experiments and asymptotic results. Illustrations with real life data sets are provided. 相似文献
998.
C. A. Glasbey 《Statistics and Computing》2009,19(1):49-56
Dynamic programming (DP) is a fast, elegant method for solving many one-dimensional optimisation problems but, unfortunately,
most problems in image analysis, such as restoration and warping, are two-dimensional. We consider three generalisations of
DP. The first is iterated dynamic programming (IDP), where DP is used to recursively solve each of a sequence of one-dimensional
problems in turn, to find a local optimum. A second algorithm is an empirical, stochastic optimiser, which is implemented
by adding progressively less noise to IDP. The final approach replaces DP by a more computationally intensive Forward-Backward
Gibbs Sampler, and uses a simulated annealing cooling schedule. Results are compared with existing pixel-by-pixel methods
of iterated conditional modes (ICM) and simulated annealing in two applications: to restore a synthetic aperture radar (SAR)
image, and to warp a pulsed-field electrophoresis gel into alignment with a reference image. We find that IDP and its stochastic
variant outperform the remaining algorithms. 相似文献
999.
Peter Hall Tapabrata Maiti 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2009,71(3):703-718
Summary. We develop a general non-parametric approach to the analysis of clustered data via random effects. Assuming only that the link function is known, the regression functions and the distributions of both cluster means and observation errors are treated non-parametrically. Our argument proceeds by viewing the observation error at the cluster mean level as though it were a measurement error in an errors-in-variables problem, and using a deconvolution argument to access the distribution of the cluster mean. A Fourier deconvolution approach could be used if the distribution of the error-in-variables were known. In practice it is unknown, of course, but it can be estimated from repeated measurements, and in this way deconvolution can be achieved in an approximate sense. This argument might be interpreted as implying that large numbers of replicates are necessary for each cluster mean distribution, but that is not so; we avoid this requirement by incorporating statistical smoothing over values of nearby explanatory variables. Empirical rules are developed for the choice of smoothing parameter. Numerical simulations, and an application to real data, demonstrate small sample performance for this package of methodology. We also develop theory establishing statistical consistency. 相似文献
1000.