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991.
On the strong convergence for weighted sums of random variables 总被引:1,自引:1,他引:0
Soo Hak Sung 《Statistical Papers》2011,52(2):447-454
A strong convergence result is obtained for weighted sums of identically distributed negatively associated random variables
which have a suitable moment condition. This result improves the result of Cai (Metrika 68:323–331, 2008). 相似文献
992.
This note provides the asymptotic distribution of a Perron-type innovational outlier unit root test developed by Popp (J Stat
Comput Sim 78:1145–1161, 2008) in case of a shift in the intercept for non-trending data. In Popp (J Stat Comput Sim 78:1145–1161,
2008), only critical values for finite samples based on Monte Carlo techniques are tabulated. Using similar arguments as in
Zivot and Andrews (J Bus Econ Stat 10:251–270, 1992), weak convergence is shown for the test statistics. 相似文献
993.
Partial linear modelling ideas have recently been adapted to situations when functional data are observed. This paper aims
to complete the study of such model by proposing a fully automatic estimation procedure. This is achieved by constructing
a data-driven method to choose the smoothing parameters entered in the nonparametric components of the model. The asymptotic
optimality of the method is stated and its practical interest is illustrated on finite size Monte Carlo simulated samples. 相似文献
994.
995.
On runs of length exceeding a threshold: normal approximation 总被引:1,自引:0,他引:1
Run statistics denoting number of runs and sum of run lengths are defined on binary sequences and their asymptotic normality
is established by a simple unified way for Bernoulli sequences. All the considered statistics share a common feature; they
refer to runs of length exceeding a specific length (a threshold). Asymptotic results of associated statistics denoting run
lengths and waiting times are derived as well. Specific probabilities of the examined statistics are used in applications
in the fields of system reliability and molecular biology. The study is illustrated by an extensive numerical experimentation. 相似文献
996.
A Bayesian multi-category kernel classification method is proposed. The algorithm performs the classification of the projections
of the data to the principal axes of the feature space. The advantage of this approach is that the regression coefficients
are identifiable and sparse, leading to large computational savings and improved classification performance. The degree of
sparsity is regulated in a novel framework based on Bayesian decision theory. The Gibbs sampler is implemented to find the
posterior distributions of the parameters, thus probability distributions of prediction can be obtained for new data points,
which gives a more complete picture of classification. The algorithm is aimed at high dimensional data sets where the dimension
of measurements exceeds the number of observations. The applications considered in this paper are microarray, image processing
and near-infrared spectroscopy data. 相似文献
997.
We propose a more efficient version of the slice sampler for Dirichlet process mixture models described by Walker (Commun.
Stat., Simul. Comput. 36:45–54, 2007). This new sampler allows for the fitting of infinite mixture models with a wide-range of prior specifications. To illustrate
this flexibility we consider priors defined through infinite sequences of independent positive random variables. Two applications
are considered: density estimation using mixture models and hazard function estimation. In each case we show how the slice
efficient sampler can be applied to make inference in the models. In the mixture case, two submodels are studied in detail.
The first one assumes that the positive random variables are Gamma distributed and the second assumes that they are inverse-Gaussian
distributed. Both priors have two hyperparameters and we consider their effect on the prior distribution of the number of
occupied clusters in a sample. Extensive computational comparisons with alternative “conditional” simulation techniques for
mixture models using the standard Dirichlet process prior and our new priors are made. The properties of the new priors are
illustrated on a density estimation problem. 相似文献
998.
Samiran Ghosh 《Statistics and Computing》2011,21(3):451-462
Lasso proved to be an extremely successful technique for simultaneous estimation and variable selection. However lasso has
two major drawbacks. First, it does not enforce any grouping effect and secondly in some situation lasso solutions are inconsistent
for variable selection. To overcome this inconsistency adaptive lasso is proposed where adaptive weights are used for penalizing
different coefficients. Recently a doubly regularized technique namely elastic net is proposed which encourages grouping effect
i.e. either selection or omission of the correlated variables together. However elastic net is also inconsistent. In this
paper we study adaptive elastic net which does not have this drawback. In this article we specially focus on the grouped selection
property of adaptive elastic net along with its model selection complexity. We also shed some light on the bias-variance tradeoff
of different regularization methods including adaptive elastic net. An efficient algorithm was proposed in the line of LARS-EN,
which is then illustrated with simulated as well as real life data examples. 相似文献
999.
Yves F. Atchadé 《Statistics and Computing》2011,21(4):463-473
In empirical Bayes inference one is typically interested in sampling from the posterior distribution of a parameter with a
hyper-parameter set to its maximum likelihood estimate. This is often problematic particularly when the likelihood function
of the hyper-parameter is not available in closed form and the posterior distribution is intractable. Previous works have
dealt with this problem using a multi-step approach based on the EM algorithm and Markov Chain Monte Carlo (MCMC). We propose
a framework based on recent developments in adaptive MCMC, where this problem is addressed more efficiently using a single
Monte Carlo run. We discuss the convergence of the algorithm and its connection with the EM algorithm. We apply our algorithm
to the Bayesian Lasso of Park and Casella (J. Am. Stat. Assoc. 103:681–686, 2008) and on the empirical Bayes variable selection of George and Foster (J. Am. Stat. Assoc. 87:731–747, 2000). 相似文献
1000.
Powerful entropy-based tests for normality, uniformity and exponentiality have been well addressed in the statistical literature.
The density-based empirical likelihood approach improves the performance of these tests for goodness-of-fit, forming them
into approximate likelihood ratios. This method is extended to develop two-sample empirical likelihood approximations to optimal
parametric likelihood ratios, resulting in an efficient test based on samples entropy. The proposed and examined distribution-free
two-sample test is shown to be very competitive with well-known nonparametric tests. For example, the new test has high and
stable power detecting a nonconstant shift in the two-sample problem, when Wilcoxon’s test may break down completely. This
is partly due to the inherent structure developed within Neyman-Pearson type lemmas. The outputs of an extensive Monte Carlo
analysis and real data example support our theoretical results. The Monte Carlo simulation study indicates that the proposed
test compares favorably with the standard procedures, for a wide range of null and alternative distributions. 相似文献