DOCSLIB.ORG

Goodness-Of-Fit Test for the Bivariate Hermite Distribution

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Goodness-Of-Fit Test for the Bivariate Hermite Distribution Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 1 October 2020 doi:10.20944/preprints202010.0016.v1 Article Goodness-of-fit test for the bivariate Hermite distribution Pablo González-Albornoz 1,†,‡ and Francisco Novoa-Muñoz 2,* 1 Universidad Adventista de Chile, Chillán, Chile; [email protected] 2 Departamento de Estadística, Universidad del Bío-Bío, Concepción, Chile; [email protected] Abstract: This paper studies the goodness of fit test for the bivariate Hermite distribution. Specifically, we propose and study a Cramér-von Mises-type test based on the empirical probability generation function. The bootstrap can be used to consistently estimate the null distribution of the test statistics. A simulation study investigates the goodness of the bootstrap approach for finite sample sizes. Keywords: Bivariate Hermite distribution; Goodness-of-fit; Empirical probability generating function; Bootstrap distribution estimator 1. Introduction The counting data can appear in different circumstances. In the univariate configuration, the Hermite distribution (HD) is a linear combination of the form Y = X1 + 2X2, where X1 and X2 are independent Poisson random variables. The properties that distinguish the HD is to be flexible when it comes to modeling counting data that present a multimodality, along with presenting several zeros, which is called zero-inflation. It also allows modeling data in which the overdispersion is moderate; that is, the variance is greater than the expected value. It was McKendrick in [9] who modeled a phagocytic experiment (bacteria count in leukocytes) through the HD, obtaining a more satisfactory model than with the Poisson distribution. However, in practice, the bivariate count data arise in several different disciplines and bivariate Hermite distribution (BHD) plays an important role, having superinflated data. For example, the accident number on two different periods [1]. Testing the goodness of fit (gof) of observations given with a probabilistic model is a crucial aspect of data analysis. For the univariate case, we have only found a single test of gof, but for data that come from a generalized Hermite distribution (for a review, see Meintanis and Bassiakos in [10]), but not from a HD. On the other hand, we did not find literature on gof tests for BHD. The purpose of this paper is to propose and study a goodness-of-fit test for the bivariate Hermite Distribution that is consistent. According to Novoa-Muñoz in [12], the probability generating function (pgf) characterizes the distribution of a random vector and can be estimated consistently by the empirical probability generating function (epgf), the proposed test is a function of the epgf. This statistical test compares the epgf of the data with an estimator of the pgf of the BHD. As it is well known, to establish the rejection region, we need to know the distribution of the statistic test. As for finite sample sizes the resulting test statistic is of the Cramér-Von Mises type, it was not possible to calculate explicitly the distribution of the statistic under null hypothesis. That is why one uses simulation techniques. Therefore, we decided to use a null approximation of the statistic by using a parametric bootstrap. Because the properties of the proposed test are asymptotic (see, for example, [11]) and with the purpose of evaluating the behavior of the test for sample of finite size, a simulation study was carried © 2020 by the author(s). Distributed under a Creative Commons CC BY license. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 1 October 2020 doi:10.20944/preprints202010.0016.v1 2 of 14 out. The present work is ordered as follows. In section2 we present some preliminary results that will serve us in the following chapters, the definition of the BHD with some of its properties is also given. In section3, the proposed statistic is presented. Section4 is devoted to showing the bootstrap estimator and its approximation to the null distribution of the statistic. Section5 is dedicated to presenting the results of a simulation study, power of a hypothesis test and the application to a set of real data. Before ending this section, we introduce some notation: FA ^ FB denotes a mixture d (compounding) distribution where FA represents the original distribution and FB the mixing distribution (i.e., the distribution of d)[2]; all vectors are row vectors and x> is the transposed of the row vector x; for any vector x, xk denotes its kth coordinate, and kxk its Euclidean norm; N0 = f0, 1, 2, 3, ...g; IfAg denotes the indicator function of the set A; Pq denotes the probability law of the BHD with parameter q; Eq denotes expectation with respect to the probability function Pq; P∗ and E∗ denote the conditional probability law and expectation, given the data (X1, Y1), ..., (Xn, Yn), L a.s. respectively; all limits in this work are taken as n ! ¥; −! denotes convergence in distribution; −! denotes almost sure convergence; let fCng be a sequence of random variables or random elements −e e −e and let e 2 R, then Cn = OP (n ) means that n Cn is bounded in probability, Cn = oP (n ) means e P −e e a.s. 2 2 that n Cn −! 0 and Cn = o(n ) means that n Cn −! 0 and H = L [0, 1] , $ denotes the separable 2 2 R 1 R 1 2 Hilbert space of the measurable functions j, $ : [0, 1] ! R such that jjjjjH = 0 0 j (t) $(t)dt < ¥. 2. Preliminaries Several definitions for the BHD have been given (see, for example, Kocherlakota and Kocherlakota in [7]). In this paper we will work with the following one, which has received more attention in the statistical literature (see, for example, Papageorgiou et al. in [13]; Kemp et al. in [4]). Let X = (X1, X2) has the bivariate Poisson distribution with the parameters dl1, dl2 and dl3 (for more details of this distribution, see for example, Johnson et al. in [3]), then X ^ N(m, s2) has the BHD. d Kocherlakota in [6] got its pgf which is given by 1 v(t; q) = exp ml + s2l2 , (1) 2 2 where t = (t1, t2), q = (m, s , l1, l2, l3), l = l1(t1 − 1) + l2(t2 − 1) + l3(t1t2 − 1) and 2 m > s (li + l3), i = 1, 2. From the pgf of the BHD, Kocherlakota and Kocherlakota [7] obtained the probability mass function of the BHD, which is given by r s min(r,s) l1l2 r s k f (r, s) = M(g) ∑ k! x Pr+s−k(g), r!s! k=0 k k where M(x) is the moment-generating function of the normal distribution, Pr(x) is a polynomial of degree r in x, g = −(l + l + l ) and x = l3 . 1 2 3 l1l2 Remark 1. If l3 = 0 then the probability function is reduced to r s l1l2 f (r, s) = M(−l − l ) P + (−l − l ). r!s! 1 2 r s 1 2 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 1 October 2020 doi:10.20944/preprints202010.0016.v1 3 of 14 Remark 2. If X is a random vector that is bivariate Hermite distributed with parameter q will be denoted X ∼ BH(q), where q 2 Q, and the parameter space is n 2 5 2 o Q = (m, s , l1, l2, l3) 2 R /m > s (li + l3), li > l3 ≥ 0, i = 1, 2 . Let X1 = (X11, X12), X2 = (X21, X22), ... , Xn = (Xn1, Xn2) be independent and identically 2 distributed (iid) random vectors defined on a probability space (W, A, P) and taking values in N0. In what follows, let 1 n ( ) = Xi1 Xi2 vn t ∑ t1 t2 n i=1 2 denote the epgf of X1, X2,..., Xn for some appropriate W ⊆ R . In the next section we will develop our statistician and for this the result given below will be fundamental, whose proof was presented in [11]. 2 X1 X2 Proposition 1. Let X1, ... , Xn be iid from a random vector X = (X1, X2) 2 N0. Let v(t) = E t1 t2 be 2 the pgf of X, defined on W ⊆ R . Let 0 ≤ bj ≤ cj < ¥, j = 1, 2, such that Q = [b1, c1] × [b2, c2] ⊆ W, then a.s. sup jvn(t) − v(t)j −! 0. t2Q 3. The test statistic and its asymptotic null distribution Let X1 = (X11, X12), X2 = (X21, X22), ... , Xn = (Xn1, Xn2) be iid from a random vector X = 2 (X1, X2) 2 N0. Based on the sample X1, X2,..., Xn, the objective is to test the hypothesis H0 : (X1, X2) ∼ BH(q), for some q 2 Q, against the alternative H1 : (X1, X2) BH(q), 8q 2 Q. With this purpose, we will recourse to some of the properties of the pgf that allow us to propose the following statistical test. According to Proposition1, a consistent estimator of the pgf is the epgf. If H0 is true and qˆn is a consistent estimator of q, then v(t; qˆn) consistently estimates the population pgf. Since the distribution 2 of X = (X1, X2) is uniquely determined by its pgf, v(t), t = (t1, t2) 2 [0, 1] , a reasonable test for testing H0 should reject the null hypothesis for large values of Vn,w(qˆn) defined by Z 1 Z 1 ˆ 2 ˆ Vn,w(qn) = Vn (t; qn)w(t)dt, (2) 0 0 where p Vn(t; q) = n fvn(t) − v(t; q)g , qˆn = qˆn(X1, X2, ... , Xn) is a consistent estimator of q and w(t) is a measurable weight function, such that w(t) ≥ 0, 8t 2 [0, 1]2, and Z 1 Z 1 w(t)dt < ¥. (3) 0 0 The assumption (3) on w ensures that the double integral in (2) is finite for each fixed n.
Load more

Recommended publications
  • Regression-Based Imputation of Explanatory Discrete Missing Data
    Regression-based imputation of explanatory discrete missing data G. Hern´andez-Herrera1,2, A. Navarro1, and D. Mori˜na∗3,4 1Research Group on Psychosocial Risks, Organization of Work and Health (POWAH), Unitat de Bioestad´ıstica, Facultat de Medicina, Universitat Aut`onoma de Barcelona 2Instituto de Investigaciones M´edicas, Facultad de Medicina, Universidad de Antioquia 3Barcelona Graduate School of Mathematics (BGSMath), Departament de Matem`atiques, Universitat Aut`onoma de Barcelona 4Department of Econometrics, Statistics and Applied Economics, Riskcenter-IREA, Universitat de Barcelona (UB) Abstract Imputation of missing values is a strategy for handling non-responses in surveys or data loss in measurement processes, which may be more effective than ignoring the losses. When the variable represents a arXiv:2007.15031v1 [stat.AP] 29 Jul 2020 count, the literature dealing with this issue is scarce. If the vari- able has an excess of zeros it is necessary to consider models includ- ing parameters for handling zero-inflation. Likewise, if problems of over- or under-dispersion are observed, generalisations of the Pois- son, such as the Hermite or Conway-Maxwell Poisson distributions are recommended for carrying out imputation. In order to assess the performance of various regression models in the imputation of a dis- crete variable based on Poisson generalisations, compared to classical ∗Corresponding Author: David Mori˜na ([email protected]) 1 counting models, this work presents a comprehensive simulation study considering a variety of scenarios and real data from a lung cancer study. To do so we compared the results of estimations using only complete data, and using imputations based on the Poisson, negative binomial, Hermite, and COMPoisson distributions, and the ZIP and ZINB models for excesses of zeros.
    [Show full text]
  • Hermite Regression Analysis of Multi-Modal Count Data
    Econometrics Working Paper EWP1001 ISSN 1485-6441 Department of Economics Hermite Regression Analysis of Multi-Modal Count Data David E. Giles* Department of Economics, University of Victoria Victoria, B.C., Canada V8W 2Y2 March, 2010 Abstract We discuss the modeling of count data whose em pirical distribution is both multi-modal and over- dispersed, and propose the Hermite distribution with covariates introduced through the conditional mean. The model is readily estimated by maximu m likelihood, and nests the Poisson model as a special case. The Hermite regression model is applied to data for the number of banking and currency crises in IMF-member countries, and is found to out-perform the Poisson and negative binomial models. Keywords: Count data, multi-modal data, over-dispersion, financial crises JEL Classifications: C16, C25, G15, G21 Author Contact: David E. Giles, Dept. of Economics, University of Victoria, P.O. Box 1700, STN CSC, Victoria, B.C., Canada V8W 2Y2; e-mail: [email protected]; Phone: (250) 721-8540; FAX: (250) 721-6214 1. Introduction Typically, models for count data (i.e., data that take only non-negative integer values) are based on distributions that do not allow for multi-modality. Obvious examples are the Poisson and negative binomial distributions. This seriously limits the usefulness of such models. We discuss a way of broadening the class of discrete distributions that are used in this field by adopting the Hermite distribution proposed by Kemp and Kemp (1965). Apart from Giles (2007), this distribution has not been used in the econometrics literature to date. Further, it appears that when it has been used in other fields, no consideration has been given to introducing covariates into the model, as is done with the conventional Poisson and negative binomial regression models.
    [Show full text]
  • Modeling of Pdfs of Non-Gaussian Data and Application to Wind Pressure Coefficients on Low-Rise Building
    MODELING OF PDFS OF NON-GAUSSIAN DATA AND APPLICATION TO WIND PRESSURE COEFFICIENTS ON LOW-RISE BUILDING By LUPING YANG A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2011 1 © 2011 Luping Yang 2 To my parents Qingcheng Yang and Chaoqun Pang 3 ACKNOWLEDGMENTS First of all, I wish to express my gratitude to my committee chair David O. Prevatt. This work would not have been accomplished without his patient guidance, continual support and great encouragement. I am also grateful to my co-chair and research advisor Dr. Kurtis R. Gurley. Working with Dr. Gurley has been a great pleasure for me. His insightful knowledge and amiable smile always gives me inspiration and encouragement when I encounter difficulties. His guidance to me has not only been limited to study and research but also extended to the way of thinking, the manner of interacting with people and the attitude of life. I wish to express my sincere gratitude to Dr. Sergei V. Shabanov in the Mathematical department for serving on my advisory committee. I would like to thank my family, Mr. and Mrs. Yang and my brother Wei Yang, for all their love, care and support. They have made me feel happy and have given me faith to study from thousands of miles away. Lastly, I wish to express my gratitude to my friends, colleagues and the hurricane research group at the University of Florida. Studying and working at UF has been a valuable and pleasant experience in my life.
    [Show full text]
  • A Comparison Between Approximations of Option Pricing Models and Risk-Neutral Densities Using Hermite Polynomials
    U.U.D.M. Project Report 2020:16 A Comparison between Approximations of Option Pricing Models and Risk-Neutral Densities using Hermite Polynomials Nathaniel Ahy Examensarbete i matematik, 30 hp Handledare: Benny Avelin Examinator: Erik Ekström Juni 2020 Department of Mathematics Uppsala University Contents 1 Introduction 1 1.1 Background of Hermite Polynomials . .1 1.2 Financial Derivatives and Stochastic Volatility . .2 1.3 Hermite Polynomials in Finance . .5 1.4 Problem Formulation . .7 1.5 Methodology . .7 1.6 Outline . .8 2 Theory 10 2.1 Heston Model . 10 2.1.1 The Fundamental Theorem of Finance . 11 2.2 Gram-Charlier Expansions . 13 2.2.1 Cumulants and an Explicit Formula for Computing them 13 2.2.2 Gram–Charlier Expansions for the Heston Model . 14 2.3 Gram-Charlier Expansion Option Pricing Formula . 14 2.4 Necula, Drimus and Farkas Risk-Neutral Measure . 15 2.4.1 A closed form formula for the coefficients for the risk- neutral density . 17 2.4.2 Implementation of Closed Form Formula . 19 3 Option Pricing 21 3.1 Estimating Coefficients through quadratic programming . 21 3.1.1 The Active Set Algorithm . 25 3.2 Fitting the NDF log mean and standard deviation to a Model 27 4 Model Fitting Results 28 4.1 Computing the Cumulants . 28 4.2 Model Errors . 29 2 Uppsala University 5 Option Data 33 5.1 Fitting the GC Parameters to the Data . 33 6 Data Fitting Results 36 6.1 NDF Errors . 36 6.2 GC errors . 40 6.3 Model Complexity . 43 7 Conclusion 45 7.1 Conclusion .
    [Show full text]
  • Multivariate Limit Theorems in the Context of Long-Range Dependence
    Multivariate limit theorems in the context of long-range dependence Shuyang Bai Murad S. Taqqu September 4, 2018 Abstract We study the limit law of a vector made up of normalized sums of functions of long-range depen- dent stationary Gaussian series. Depending on the memory parameter of the Gaussian series and on the Hermite ranks of the functions, the resulting limit law may be (a) a multivariate Gaussian process involv- ing dependent Brownian motion marginals, or (b) a multivariate process involving dependent Hermite processes as marginals, or (c) a combination. We treat cases (a), (b) in general and case (c) when the Hermite components involve ranks 1 and 2. We include a conjecture about case (c) when the Hermite ranks are arbitrary. 1 Introduction A stationary time series displays long-range dependence if its auto-covariance decays slowly or if its spectral density diverges around the zero frequency. When there is long-range dependence, the asymptotic limits of various estimators are often either Brownian Motion or a Hermite process. The most common Hermite processes are fractional Brownian motion (Hermite process of order 1) and the Rosenblatt process (Hermite process of order 2), but there are Hermite processes of any order. Fractional Brownian motion is the only Gaussian Hermite process. Most existing limit theorems involve univariate convergence, that is, convergence to a single limit process, for example, Brownian motion or a Hermite process ([3, 5, 24]). In time series analysis, however, one often needs joint convergence, that is, convergence to a vector of processes. This is because one often needs to consider different statistics of the process jointly.
    [Show full text]
  • Good Distribution Modelling with the R Package Good
    GOOD DISTRIBUTION MODELLING WITH THE R PACKAGE good Jordi Tur, David Moriña, Pedro Puig, Alejandra Cabaña, Argimiro Arratia, Amanda Fernández-Fontelo ABSTRACT Although models for count data with over-dispersion have been widely considered in the literature, models for under-dispersion -the opposite phenomenon- have received less attention as it is only rela- tively common in particular research fields such as biodosimetry and ecology. The Good distribution is a flexible alternative for modelling count data showing either over-dispersion or under-dispersion, although no R packages are still available to the best of our knowledge. We aim to present in the following the R package good that computes the standard probabilistic functions (i.e., probability density function, cumulative distribution function, and quantile function) and generates random samples from a population following a Good distribution. The package also considers a function for Good regression, including covariates in a similar way to that of the standard glm function. We finally show the use of such a package with some real-world data examples addressing both over-dispersion and especially under-dispersion. Keywords Good distribution · discrete-valued data · underdispersion · regression 1 Introduction Even though the Poisson distribution is the most broadly used for modelling count data (e.g., properties like closure under addition make this distribution especially useful in practice) and has been used to model applications such as the number of soldiers killed by horse kicks per Prussian army corp ([1]) or the number of chromosome aberrations (e.g., dicentrics or rings) in scored sample cells from potentially irradiated individuals ([2]), it is usually chosen for tradition and convenience, and such a practice can very often be brought into question.
    [Show full text]
  • Compound Poisson Point Processes, Concentration and Oracle Inequalities
    Compound Poisson Point Processes, Concentration and Oracle Inequalities Huiming Zhanga, Xiaoxu Wub aSchool of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing, 100871, China bMathematics Department, Rutgers University, Piscataway, NJ 08854, USA Abstract This note aims at presenting several new theoretical results for the compound Poisson point process, which follows the work of Zhang et al. [Insurance Math. Econom. 59(2014), 325-336]. The first part provides a new characterization for a discrete compound Poisson point process (proposed by Acz´el[Acta Math. Hun- gar. 3(3)(1952), 219-224]), it extends the characterization of the Poisson point process given by Copeland and Regan [Ann. Math. (1936): 357-362]. Next, we derive some concentration inequalities for discrete com- pound Poisson point process (negative binomial random variable with unknown dispersion is a significant example). These concentration inequalities are potentially useful in count data regressions. We give an ap- plication in the weighted Lasso penalized negative binomial regression whose KKT conditions of penalized likelihood hold with high probability and then we derive non-asymptotic oracle inequalities for a weighted Lasso estimator. Keywords: characterization of point processes, Poisson random measure, concentration inequalities, sub-Gamma random variables, high-dimensional negative binomial regressions. 2010 Mathematics Subject Classification: 60G55, 60G51, 62J12 1. Introduction Powerful and useful concentration inequalities of empirical processes have been derived one after another for several decades. Various types of concentration inequalities are mainly based on mo- ments condition (such as sub-Gaussian, sub-exponential and sub-Gamma) and bounded difference condition; see Boucheron et al. (2013). Its central task is to evaluate the fluctuation of empirical processes from some value (for example, the mean and median) in probability.
    [Show full text]
  • Generalized Multivariate Hermite Distributions and Related Point Processes
    Ann. Inst. Statist. Math. Vol. 45, No. 2, 367-381 (1993) GENERALIZED MULTIVARIATE HERMITE DISTRIBUTIONS AND RELATED POINT PROCESSES R. K. MILNE 1 AND M. WESTCOTT 2 1Department of Mathematies~ University of Western Australia, Nedlands 6009, Australia 2Division of Mathematics and Statistics, CSIRO, P.O. Box 1965, Canberra City 2601, Australia (Received July 15, 1990; revised June 19, 1992) Abstract. This paper is primarily concerned with the problem of character- izing those functions of the form G(z) = exp { 0<k'l<m~ ak(zk--1)} ' where z = [zl,..., z~]', which are probability generating functions. The cor- responding distributions are called generalized multivariate Hermite distribu- tions. Use is made of results of Cuppens (197.5), with particular interest at- taching to the possibility of some of the coefficients ak being negative. The paper goes on to discuss related results for point processes. The point process analogue of the above characterization problem was raised by Milne and Westcott (1972). This problem is not solved but relevant examples are presented. Ammann and Thall (1977) and Waymire and Gupta (1983) have established a related characterization result for certain infinitely divisible point processes. Their results are considered from a probabilistic viewpoint. Key words and phrases: Hermite distribution, generalized multivariate Her- mite distribution, point process, probability generating function. 1. Introduction Consider a probability generating function (p.g.fn) of the form (1.1) G(z) = exp{P(z) - P(1)} where P(z) is a polynomial in z E R ~, and 1 = [1,..., 1]'. When all the coeffi- cients of P(z) are non-negative, G(z) is a p.g.fn; in fact it is the p.g.fn of a com- pound Poisson distribution.
    [Show full text]
  • Nonparametric Distribution Function Estimation
    Masterarbeit Nonparametric Distribution Function Estimation Nichtparametrische Schätzung von Verteilungsfunktionen Ariane Hanebeck 05.04.2020 Betreuung: PD Dr. Bernhard Klar Fakultät für Mathematik Institut für Stochastik Karlsruher Institut für Technologie Contents 1 Introduction 3 1.1 The Different Estimators . .4 2 Empirical Distribution Function 5 3 Kernel Estimation 6 3.1 Asymptotic Behavior . .7 3.2 Asymptotically Optimal h with Respect to MSE . .8 3.3 Asymptotically Optimal h with Respect to MISE . .9 4 Function Estimation With Bernstein Polynomials 11 5 Bernstein Distribution Function Estimation on [0, 1] 13 5.1 General Properties . 14 5.1.1 Bias and Variance . 16 5.2 Asymptotic Behavior . 18 5.3 Asymptotically Optimal m with Respect to MSE . 19 5.4 Asymptotically Optimal m with Respect to MISE . 20 5.5 Difference of the Distribution Estimator to the Density Estimator . 21 5.6 Deficiency . 21 5.7 Another Way to Choose the Optimal m .......................... 23 5.7.1 Local Choice of m ................................. 23 5.7.2 Global Choice of m ................................. 25 5.7.3 Asymptotic Properties . 25 5.7.4 Simulation . 26 5.8 Properties of Pk,m ...................................... 27 5.9 Proofs Bernstein . 32 6 Szasz Distribution Function Estimation on Half Line 41 6.1 General Properties . 42 6.1.1 Bias and Variance . 44 6.2 Asymptotic Behavior . 45 6.3 Asymptotically Optimal m with Respect to MSE . 46 6.4 Asymptotically Optimal m with Respect to MISE . 47 6.5 Deficiency . 48 6.6 Properties of Vk,m ...................................... 50 6.7 Proofs Szasz . 55 7 Hermite Distribution Function Estimation 60 7.1 Hermite Polynomials .
    [Show full text]
  • Behavior with Respect to the Hurst Index of the Wiener Hermite Integrals and Application to Spdes Meryem Slaoui, Ciprian A
    Behavior with respect to the Hurst index of the Wiener Hermite integrals and application to SPDEs Meryem Slaoui, Ciprian A. Tudor To cite this version: Meryem Slaoui, Ciprian A. Tudor. Behavior with respect to the Hurst index of the Wiener Hermite integrals and application to SPDEs. Journal of Mathematical Analysis and Applications, Elsevier, 2019, 10.1016/j.jmaa.2019.06.031. hal-02181051 HAL Id: hal-02181051 https://hal.archives-ouvertes.fr/hal-02181051 Submitted on 11 Jul 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Behavior with respect to the Hurst index of the Wiener Hermite integrals and application to SPDEs Meryem Slaoui and C. A. Tudor 1 Laboratoire Paul Painlev´e, Universit´ede Lille 1 F-59655 Villeneuve d’Ascq, France. [email protected] [email protected] Abstract We consider the Wiener integral with respect to a d-parameter Hermite process 1 d with Hurst multi-index H = (H1, .., Hd) 2 , 1 and we analyze the limit behavior in ∈ 1 distribution of this object when the components of H tend to 1 and/or 2 . As examples, we focus on the solution to the stochastic heat equation with additive Hermite noise and to the Hermite Ornstein-Uhlenbeck process.
    [Show full text]
  • Some Properties of the Generalized Stuttering Poisson Distribution and Its Applications
    CORE Metadata, citation and similar papers at core.ac.uk Provided by CSCanada.net: E-Journals (Canadian Academy of Oriental and Occidental Culture, Canadian Research & Development Center of Sciences and Cultures) ISSN 1923-8444 [Print] Studies in Mathematical Sciences ISSN 1923-8452 [Online] Vol. 5, No. 1, 2012, pp. [11{26] www.cscanada.net DOI: 10.3968/j.sms.1923845220120501.Z0697 www.cscanada.org Some Properties of the Generalized Stuttering Poisson Distribution and Its Applications ZHANG Huiming[a],[b],*, CHU Lili[b] and DIAO Yu[b] [a] Department of Economics, Central China Normal University, China. [b] Department of Mathematics and Statistics, Central China Normal University, China. * Corresponding author. Address: Department of Economics, Central China Normal University, No. 152 Luoyu Street, Wuhan, 430079, China; E-Mail: [email protected] Received: May 31, 2012/ Accept: July 25, 2012/ Published: August 31, 2012 Abstract: Based on the probability generating function of stuttering Poisson distribution (SPD), this paper considers some equivalent propositions of SPD. From this, we show that some distributions in the application of non-life insurance actuarial science are SPD, such as negative binomial distribution, compound Poisson distribution etc.. By weakening condition of equivalent propositions of SPD, we define the generalized SPD and prove that any non-negative discrete random variable X with P fX = 0g > 0:5 obey generalized SPD. Then, we discuss the waiting time distribution of generalized stuttering Poisson process. We consider cumulant estimation of generalized SPD's parameters. As an application, we use SPD with four parameters (4th SPD) to fit auto insurance claim data. The fitting results show that 4th SPD is more accurate than negative binomial and Poisson distribution.
    [Show full text]
  • G Geometric Distribution Has Proba- Bility Function Given By: L 1 X Pxl = Bbll, (5) ] G 11++L L X = 0,1,…, L > 0
    EM ALGORITHM FOR MIXED POISSON AND OTHER DISCRETE DISTRIBUTIONS BY DIMITRIS KARLIS ABSTRACT Mixed Poisson distributions are widely used in various disciplines including actuarial applications. The family of mixed Poisson distributions contains several members according to the choice of the mixing distribution for the parameter of the Poisson distribution. Very few of them have been studied in depth, mainly because of algebraic intractability. In this paper we will describe an EM type algorithm for maximum likelihood estimation for mixed Poisson distributions. The main achievement is that it reduces the problem of estimation to one of estimation of the mixing distribution which is usually easier. Variants of the algorithm work even when the probability function of the mixed distri- bution is not known explicitly but we have only an approximation of it. Other discrete distributions are treated as well. KEYWORDS Poisson mixtures, beta-binomial distribution, Yule distribution, Monte Carlo EM. 1. INTRODUCTION Starting from a distribution family f (x|q) we may obtain a very rich new fam- ily of distributions if we allow the parameter q to be itself a random variable with distribution function G(q|f) depending on a vector of parameters f. Note that q is not necessarily a scalar and it can be vector-valued. Then the uncon- ditional distribution of x will be given by fx]]qqqfgg= # fx dG^ h (1) Usually, f (x|f) is called the mixed distribution while G(q|f) is called the mixing distribution. Mixture models are also called overdispersion models, because keeping the mean fixed they have variances larger than the original model.
    [Show full text]