In actuarial hteramre, researchers suggested various statistical procedures to estimate the parameters in claim count or frequency model. In particular, the Poisson regression model, which is also known as the Generahzed Linear Model (GLM) with Poisson error structure, has been x~adely used in the recent years. However, it is also recognized that the count or frequency data m insurance practice...

Let us begin by recalling the definition of two familiar processes, a Brownian motion and a Poisson process. A real-valued process B = {B t : t ≥ 0} defined on a probability space (Ω, F , P) is said to be a Brownian motion if the following hold: (i) The paths of B are P-almost surely continuous. (ii) P(B 0 = 0) = 1. (iii) For 0 ≤ s ≤ t, B t − B s is equal in distribution to B t−s. (iv) For 0 ≤ ...

In this paper, we prove a local limit theorem for the ratio of Poisson distribution to Gaussian with same mean and variance, using only elementary methods (Taylor expansions Stirling's formula). We then apply result derive an upper bound on Le Cam distance between experiments, which gives complete proof sketch provided in unpublished set lecture notes by Pollard (2010), who uses different appro...

Power and sample size calculation is an essential component of experimental design in biomedical research. For RNA-sequencing experiments, sample size calculations have been proposed based on mathematical models such as Poisson and negative binomial; however, RNA-seq data has exhibited variations, i.e. over-dispersion, that has caused past calculation methods to be underor over-power. Because o...

We investigate the Rényi entropy of sums independent integer-valued random variables through Fourier theoretic means, and give sharp comparisons between variance for Bernoulli variables. As applications, we prove that a discrete “min-entropy power” is superadditive with respect to convolution modulo universal constant, new bounds on an entropic generalization Littlewood-Offord problem are in “P...

BACKGROUND Geostatistical techniques are now available to account for spatially varying population sizes and spatial patterns in the mapping of disease rates. At first glance, Poisson kriging represents an attractive alternative to increasingly popular Bayesian spatial models in that: 1) it is easier to implement and less CPU intensive, and 2) it accounts for the size and shape of geographical ...

We propose a nonparametric estimator of the expected discounted penalty function in compound Poisson risk model. use projection on Laguerre basis and we compute coefficients using Plancherel theorem. provide an upper bound MISE our estimator, show it achieves parametric rates convergence Sobolev–Laguerre spaces without needing bias-variance compromise. Moreover, compare with deconvolution metho...

The Poisson regression is a popular approach in modeling count data. However, many situations often the variance of data greater than mean (over-dispersed data) and generalized or mixed models such as gamma (negative binomial), inverse Gaussian, lognormal, Lindley have been proposed alternatives to for describing over-dispersed In some situations, source over-dispersion large percentage zeros d...

We introduce a plane, which we call the delta-sigma plane, that is indexed by the norm of the estimator bias gradient and the variance of the estimator. The norm of the bias gradient is related to the maximum variation in the estimator bias function over a neighborhood of parameter space. Using a uniform Cramer-Rao (CR) bound on estimator variance a delta-sigma tradeoo curve is speciied which d...

The Bayes factor is a useful summary for model selection. Calculation of this measure involves evaluating the integrated likelihood (or prior predictive density), which can be estimated from the output of MCMC and other posterior simulation methods using the harmonic mean estimator. vVhile this is a simulation-consistent estimator, it can have infinite variance. In this article we describe a me...