2期

最新文章

• On sequential designs in nonlinear problems AI翻译
  作者：Hu, I
  作者单位：Hong Kong University of Science & Technology
  摘要：In this paper we study the consistency of parameter estimators in sequential nonlinear design. Our main tool is a recursion which holds for the sequence of posterior variances. Under mild conditions, we establish the strong consistency of Bayes estimators in stochastic regression models which cover a broad range of nonlinear problems. Four examples from nonlinear sequential design are discussed.
• Poisson/gamma random field models for spatial statistics AI翻译
  作者：Wolpert, RL; Ickstadt, K
  作者单位：Duke University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Doubly stochastic Bayesian hierarchical models are introduced to account for:uncertainty and spatial variation in the underlying intensity measure for-point process models. Inhomogeneous gamma process random fields and, more generally, Markov random fields with infinitely divisible distributions are used to construct positively autocorrelated intensity measures for spatial Poisson point processes; these in turn are used; to model the number and location of individual events. A data augmentatio...
• Measuring heterogeneity in forensic databases using hierarchical Bayes models AI翻译
  作者：Roeder, K; Escobar, M; Kadane, JB; Balazs, I
  作者单位：Carnegie Mellon University; University of Toronto; Carnegie Mellon University
  摘要：As currently defined, DNA fingerprint profiles do not uniquely identify individuals. For criminal cases involving DNA evidence, forensic scientists evaluate the conditional probability that an unknown, but distinct, individual matches the crime sample, given that the defendant matches. Estimates of the conditional probability of observing matching profiles are based on reference populations maintained by forensic testing laboratories. Each of these databases is heterogeneous, being composed of...
• Clustering and abundance estimation for Neyman-Scott models and line transect surveys AI翻译
  作者：Brown, BM; Cowling, A
  作者单位：University of Tasmania; Commonwealth Scientific & Industrial Research Organisation (CSIRO)
  摘要：This paper considers the estimation of clustering parameters and mean species intensity based on likelihood theory for the simplified Neyman-Scott Poisson model, with observations taken from line transect surveys with a Gaussian detection function. The estimators and accompanying standard error expressions are tractable and easy to calculate, and, coming from likelihood methods, often will have high efficiency. Such properties compare favourably with those of existing K-function methods which ...