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991.
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
992.
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
993.
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
994.
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
995.
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
996.
Under appropriate long range dependence conditions, the point process of exceedances of a stationary sequence weakly converges to a homogeneous compound Poisson point process. This limiting point process can be characterized by the extremal index and the cluster-size probabilities. In this paper we address the problem of estimating these quantities and we consider the intervals estimators introduced in Ferro and Segers [2003. Inference for clusters of extreme values. J. Roy. Statist. Soc. Ser. B 545–556] and in Ferro [2004. Statistical methods for clusters of extreme values. Ph.D. Thesis, Lancaster University]. We establish asymptotic weak convergence to Gaussian random variables and we give their asymptotic variance. 相似文献
997.
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
998.
999.
Daniela M. Witten Robert Tibshirani 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2009,71(3):615-636
Summary. We propose covariance-regularized regression, a family of methods for prediction in high dimensional settings that uses a shrunken estimate of the inverse covariance matrix of the features to achieve superior prediction. An estimate of the inverse covariance matrix is obtained by maximizing the log-likelihood of the data, under a multivariate normal model, subject to a penalty; it is then used to estimate coefficients for the regression of the response onto the features. We show that ridge regression, the lasso and the elastic net are special cases of covariance-regularized regression, and we demonstrate that certain previously unexplored forms of covariance-regularized regression can outperform existing methods in a range of situations. The covariance-regularized regression framework is extended to generalized linear models and linear discriminant analysis, and is used to analyse gene expression data sets with multiple class and survival outcomes. 相似文献
1000.
The variational approach to Bayesian inference enables simultaneous estimation of model parameters and model complexity. An interesting feature of this approach is that it also leads to an automatic choice of model complexity. Empirical results from the analysis of hidden Markov models with Gaussian observation densities illustrate this. If the variational algorithm is initialized with a large number of hidden states, redundant states are eliminated as the method converges to a solution, thereby leading to a selection of the number of hidden states. In addition, through the use of a variational approximation, the deviance information criterion for Bayesian model selection can be extended to the hidden Markov model framework. Calculation of the deviance information criterion provides a further tool for model selection, which can be used in conjunction with the variational approach. 相似文献