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31.
To reduce nonresponse bias in sample surveys, a method of nonresponse weighting adjustment is often used which consists of multiplying the sampling weight of the respondent by the inverse of the estimated response probability. The authors examine the asymptotic properties of this estimator. They prove that it is generally more efficient than an estimator which uses the true response probability, provided that the parameters which govern this probability are estimated by maximum likelihood. The authors discuss variance estimation methods that account for the effect of using the estimated response probability; they compare their performances in a small simulation study. They also discuss extensions to the regression estimator. 相似文献
32.
A novel framework is proposed for the estimation of multiple sinusoids from irregularly sampled time series. This spectral analysis problem is addressed as an under-determined inverse problem, where the spectrum is discretized on an arbitrarily thin frequency grid. As we focus on line spectra estimation, the solution must be sparse, i.e. the amplitude of the spectrum must be zero almost everywhere. Such prior information is taken into account within the Bayesian framework. Two models are used to account for the prior sparseness of the solution, namely a Laplace prior and a Bernoulli–Gaussian prior, associated to optimization and stochastic sampling algorithms, respectively. Such approaches are efficient alternatives to usual sequential prewhitening methods, especially in case of strong sampling aliases perturbating the Fourier spectrum. Both methods should be intensively tested on real data sets by physicists. 相似文献
33.
James P. McDermott G. Jogesh Babu John C. Liechty Dennis K. J. Lin 《Statistics and Computing》2007,17(4):311-321
We consider the problem of density estimation when the data is in the form of a continuous stream with no fixed length. In
this setting, implementations of the usual methods of density estimation such as kernel density estimation are problematic.
We propose a method of density estimation for massive datasets that is based upon taking the derivative of a smooth curve
that has been fit through a set of quantile estimates. To achieve this, a low-storage, single-pass, sequential method is proposed
for simultaneous estimation of multiple quantiles for massive datasets that form the basis of this method of density estimation.
For comparison, we also consider a sequential kernel density estimator. The proposed methods are shown through simulation
study to perform well and to have several distinct advantages over existing methods. 相似文献
34.
Andris Abakuks 《Journal of the Royal Statistical Society. Series A, (Statistics in Society)》2007,170(3):841-850
Summary. In New Testament studies, the synoptic problem is concerned with the relationships between the gospels of Matthew, Mark and Luke. In an earlier paper a careful specification in probabilistic terms was set up of Honoré's triple-link model. In the present paper, a modification of Honoré's model is proposed. As previously, counts of the numbers of verbal agreements between the gospels are examined to investigate which of the possible triple-link models appears to give the best fit to the data, but now using the modified version of the model and additional sets of data. 相似文献
35.
Jason P. Fine David V. Glidden Kristine E. Lee 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2003,65(1):317-329
Summary. We propose a simple estimation procedure for a proportional hazards frailty regression model for clustered survival data in which the dependence is generated by a positive stable distribution. Inferences for the frailty parameter can be obtained by using output from Cox regression analyses. The computational burden is substantially less than that of the other approaches to estimation. The large sample behaviour of the estimator is studied and simulations show that the approximations are appropriate for use with realistic sample sizes. The methods are motivated by studies of familial associations in the natural history of diseases. Their practical utility is illustrated with sib pair data from Beaver Dam, Wisconsin. 相似文献
36.
李旭燕 《广西师范学院学报(哲学社会科学版)》2008,29(4):152-156
性向解释是在频率解释不能解决单个事件概率的情形下由波普尔提出来的。虽然波普尔的性向解释也没有解决为单个事件引入客观概率的问题,但与频率解释相比,它对概率论应用的情形提供了有用的扩展,并且“性向”这个术语并不仅仅适用于他自己的观点。由此,后期波普尔、米勒和费塞特发展了单个事例性向解释,吉利斯发展了长趋势性向解释。 相似文献
37.
CATIA SCRICCIOLO 《Scandinavian Journal of Statistics》2007,34(3):626-642
Abstract. We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric approach. We define a Dirichlet mixture prior that, while selecting piecewise constant densities, has full support on the Hellinger metric space of all commonly dominated probability measures on a known bounded interval. We derive pointwise rates of convergence for the posterior expected density by studying the speed at which the posterior mass accumulates on shrinking Hellinger neighbourhoods of the sampling density. If the data are sampled from a strictly positive, α -Hölderian density, with α ∈ ( 0,1] , then the optimal convergence rate n− α / (2 α +1) is obtained up to a logarithmic factor. Smoothing histograms by polygons, a continuous piecewise linear estimator is obtained that for twice continuously differentiable, strictly positive densities satisfying boundary conditions attains a rate comparable up to a logarithmic factor to the convergence rate n −4/5 for integrated mean squared error of kernel type density estimators. 相似文献
38.
The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical
data are intrinsically stochastic and tend to be observed by means of discrete-time sampling systems, which are often limited
in their completeness. In this paper we explore how to make Bayesian inference for the kinetic rate constants of regulatory
networks, using the stochastic kinetic Lotka-Volterra system as a model. This simple model describes behaviour typical of
many biochemical networks which exhibit auto-regulatory behaviour. Various MCMC algorithms are described and their performance
evaluated in several data-poor scenarios. An algorithm based on an approximating process is shown to be particularly efficient. 相似文献
39.
Robin Willink 《Revue canadienne de statistique》2008,36(4):623-637
If the unknown mean of a univariate population is sufficiently close to the value of an initial guess then an appropriate shrinkage estimator has smaller average squared error than the sample mean. This principle has been known for some time, but it does not appear to have found extension to problems of interval estimation. The author presents valid two‐sided 95% and 99% “shrinkage” confidence intervals for the mean of a normal distribution. These intervals are narrower than the usual interval based on the Student distribution when the population mean lies in such an “effective interval.” A reduction of 20% in the mean width of the interval is possible when the population mean is sufficiently close to the value of the guess. The author also describes a modification to existing shrinkage point estimators of the general univariate mean that enables the effective interval to be enlarged. 相似文献
40.
JØRUND GÅSEMYR 《Scandinavian Journal of Statistics》2003,30(1):159-173
In this paper, we present a general formulation of an algorithm, the adaptive independent chain (AIC), that was introduced in a special context in Gåsemyr et al . [ Methodol. Comput. Appl. Probab. 3 (2001)]. The algorithm aims at producing samples from a specific target distribution Π, and is an adaptive, non-Markovian version of the Metropolis–Hastings independent chain. A certain parametric class of possible proposal distributions is fixed, and the parameters of the proposal distribution are updated periodically on the basis of the recent history of the chain, thereby obtaining proposals that get ever closer to Π. We show that under certain conditions, the algorithm produces an exact sample from Π in a finite number of iterations, and hence that it converges to Π. We also present another adaptive algorithm, the componentwise adaptive independent chain (CAIC), which may be an alternative in particular in high dimensions. The CAIC may be regarded as an adaptive approximation to the Gibbs sampler updating parametric approximations to the conditionals of Π. 相似文献