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排序方式: 共有218条查询结果,搜索用时 265 毫秒
31.
Let X
1, X
2,... be iid random variables (rv's) with the support on nonnegative integers and let (W
n
, n≥0) denote the corresponding sequence of weak record values. We obtain new characterization of geometric and some other discrete
distributions based on different forms of partial independence of rv's W
n
and W
n+r
—W
n
for some fixed n≥0 and r≥1. We also prove that rv's W
0 and W
n+1
—W
n
have identical distribution if and only if (iff) the underlying distribution is geometric. 相似文献
32.
This paper studies general sufficient conditions for the geometric ergodicity and the existence of moments for a class of nonlinear autoregressive models with nonlinear ARCH errors. Applications of these conditions to various well-known nonlinear time series models yield specific sufficient conditions, many of which are new or generalizations of existing conditions. 相似文献
33.
对群组判断矩阵提出一种新的最小最大几何距离排序方法(MGDM)。鉴于不同专家所给判断矩阵质量上的差异,MGDM对群组判断矩阵进行不同程度的加权处理,并进行群组一致性检验。 相似文献
34.
Bayesian Survival Analysis Using Bernstein Polynomials 总被引:1,自引:0,他引:1
I-SHOU CHANG CHAO A. HSIUNG YUH-JENN WU CHE-CHI YANG 《Scandinavian Journal of Statistics》2005,32(3):447-466
Abstract. Bayesian survival analysis of right-censored survival data is studied using priors on Bernstein polynomials and Markov chain Monte Carlo methods. These priors easily take into consideration geometric information like convexity or initial guess on the cumulative hazard functions, select only smooth functions, can have large enough support, and can be easily specified and generated. Certain frequentist asymptotic properties of the posterior distribution are established. Simulation studies indicate that these Bayes methods are quite satisfactory. 相似文献
35.
In Markov chain Monte Carlo analysis, rapid convergence of the chain to its target distribution is crucial. A chain that converges geometrically quickly is geometrically ergodic. We explore geometric ergodicity for two-component Gibbs samplers (GS) that, under a chosen scanning strategy, evolve through one-at-a-time component-wise updates. We consider three such strategies: composition, random sequence, and random scans. We show that if any one of these scans produces a geometrically ergodic GS, so too do the others. Further, we provide a simple set of sufficient conditions for the geometric ergodicity of the GS. We illustrate our results using two examples. 相似文献
36.
In this paper we present first order autoregressive (AR(1)) time series with negative binomial and geometric marginals. These processes are the discrete analogues of the gamma and exponential processes introduced by Sim (1990). Many properties of the processes discussed here, such as autocorrelation, regression and joint distributions, are studied. 相似文献
37.
38.
We propose a new integer-valued time series process, called generalized pth-order random coefficient integer-valued autoregressive process with signed thinning operator. This kind of process is appropriate for modeling negative integer-valued time series; strict stationarity and ergodicity of the process are established. Estimators of the model's parameters are derived and their properties are studied via simulation. We apply our process to a real data example. 相似文献
39.
In this study, the robustness of power and significance level of several statistical testing methods was evaluated under the assumption that the test populations were from Poisson, negative binomial, or geometric distributions. The F-ratio test, with or without appropriate transformations, was shown to be both safe and robust for all distributions examined. 相似文献
40.
In this article, we consider clustering based on principal component analysis (PCA) for high-dimensional mixture models. We present theoretical reasons why PCA is effective for clustering high-dimensional data. First, we derive a geometric representation of high-dimension, low-sample-size (HDLSS) data taken from a two-class mixture model. With the help of the geometric representation, we give geometric consistency properties of sample principal component scores in the HDLSS context. We develop ideas of the geometric representation and provide geometric consistency properties for multiclass mixture models. We show that PCA can cluster HDLSS data under certain conditions in a surprisingly explicit way. Finally, we demonstrate the performance of the clustering using gene expression datasets. 相似文献