Convolutions of α-unimodal discrete distributions

This note proves that the convolution of an α-unimodal discrete distribution with a β-unimodal discrete distribution is (α+β)-unimodal whenever (α+β)≥1. This is the discrete analogue of the fundamental result on generalized unimodality given by Olshen and Savage (1970).     

A Model for Positively Correlated Count Variables

An α-permanental random field is briefly speaking a model for a collection of non-negative integer valued random variables with positive associations. Though such models possess many appealing probabilistic properties, many statisticians seem unaware of α-permanental random fields and their potential applications. The purpose of this paper is to summarize useful probabilistic results, study stochastic constructions and simulation techniques, and discuss some examples of α-permanental random fields. This should provide a useful basis for discussing the statistical aspects in future work.     

First-Order Integer-Valued Autoregressive (INAR (1)) Process: Distributional and Regression Properties

Some properties of a first-order integer-valued autoregressive process (INAR)) are investigated. The approach begins with discussing the self-decomposability and unimodality of the 1-dimensional marginals of the process {X_n} generated according to the scheme X_n=α° X _n-i +e_n, where α° X _n-1 denotes a sum of X_{n - 1}, independent 0 - 1 random variables Y^(n-1), independent of X _n-1 with Pr -( y ^{(n - 1)}= 1) = 1 - Pr ( y ^(n-i)= 0) =α. The distribution of the innovation process ( e _n) is obtained when the marginal distribution of the process ( X _n) is geometric. Regression behavior of the INAR(1) process shows that the linear regression property in the backward direction is true only for the Poisson INAR(1) process.     

Signed Central Moments of Integer Random Variables with Decreasing Density

Signed central α-moments of integer valued rv with decreasing density are considered. These are all positive if α≥3/2. We state sharp universal bounds on α depending only on the expectation of the random variable. For special cases the bounds are also evaluated numerically.     

Some results on discrete a–monotonicity

Some characterizations and preservation properties of a–monotonicity are investigated. In particular it is shown that a mixture of binomial or Poisson distributions preserves the a–monotonicity of the mixing distribution. Also, a characterization of a class of infinitely divisible distributions, within the discrete α–unimodal class, is given. This characterization is the discrete version of that of ALAMATSAZ (1985) and KOTZ and STEUTEL (1988) in the continuous case.     

Goodness of Fit for Discrete Random Variables Using the Conditional Density

In this paper we find a new test of goodness of fit in the case of discrete random variables. The main advantage of the methodology proposed in this paper relies on the fact that given the sample, we can control the probability of the type I error, that is α, and then find the exact value of the probability of the type II error, β, associated, in some cases. The results are not asymptotic, but exact. Also a conditional test for two alternatives is obtained. We also include some simulations in order to check the power of the procedures.Mathematics Subject Classification (2000) Primary 62G10 · 62B05 · Secondary 62E10     

Asymptotic expansion of S-estimators of location and covariance

By means of a straightforward application of empirical process theory, we show that S-estimators of multivariate location and covariance are asymptotically equivalent to a sum of independent vector and matrix valued random elements respectively. This provides an alternative proof of asymptotic normality of S-estimators and clearly explains the limiting covariance structure. It also leads to a relatively simple proof of asymptotic normality of the length of the shortest α-fraction.     

Distributional Properties of Record Values from the Geometric Distribution

Some distributional properties of the record values of non–identically distributed random variables having geometric distributions are discussed. Three theorems dealing with the characterization of the geometric distribution based on these distributional properties are presented. The unique minimum variance unbiased estimators of some functions of the parameters of the distribution and various predictors of the s–th record valued utilizing the first m(m相似文献   

A note on mixed Poisson approximation

Conditions sufficient for the weak convergence of sums of 0–1 random variables indexed by k -sets to a mixed Poisson law are presented. An example of applications concerning "distance" random variables is also provided.     

On divergence and convergence of sums of nonnegative random variables

Abstract  If X ₁, X ₂,… are exponentially distributed random variables thenσ^∞_{k= 1} X_k=∞ with probability 1 iff σ^∞_{k= 1} EX_k=∞. This result, which is basic for a criterion in the theory of Markov jump processes for ruling out explosions (infinitely many transitions within a finite time) is usually proved under the assumption of independence (see FREEDMAN (1971), p. 153–154 or BREI-MAN (1968), p. 337–338), but is shown in this note to hold without any assumption on the joint distribution. More generally, it is investigated when sums of nonnegative random variables with given marginal distributions converge or diverge whatever are their joint distributions.     

Distributions characterized through conditional expectations

Using conditional expectations, we present results that lead to the characterization of several distributions. Both absolutely continuous random variables and discrete random variables are considered. In the case of absolutely continuous random variables, the results lead to the characterization of a family of distributions while in the case of discrete random variables, the distribution is almost uniquely determined under the stated conditions.     

Mixed MNL models for discrete response

This paper considers mixed, or random coefficients, multinomial logit (MMNL) models for discrete response, and establishes the following results. Under mild regularity conditions, any discrete choice model derived from random utility maximization has choice probabilities that can be approximated as closely as one pleases by a MMNL model. Practical estimation of a parametric mixing family can be carried out by Maximum Simulated Likelihood Estimation or Method of Simulated Moments, and easily computed instruments are provided that make the latter procedure fairly efficient. The adequacy of a mixing specification can be tested simply as an omitted variable test with appropriately defined artificial variables. An application to a problem of demand for alternative vehicles shows that MMNL provides a flexible and computationally practical approach to discrete response analysis. Copyright © 2000 John Wiley & Sons, Ltd.     

Mean,median, mode IV

This note continues the discussion about the well-known mean-median-mode inequality (Runnenburg , 1978, van Zwet , 1979, and Dharmadhikari and Joag-dev , 1983). A discrete analogue of this inequality is proved under similar conditions as those used for the classical Khinchin unimodality on R .     

Dominanza stocastica con avversione al rischio

In ques to la voro si introduce la classeU delle funzioni reali definite in un reticolo che sono funzioni di utilità di una classe di decisorti detti avversi al rischio e si caratterizza la dominancza stocastica secondo la classeU.

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Summary The aim of this paper is to generalize a result of [4] about stochastic dominance under the assumption of risk aversion. The generalization consists in considering the case of random variables defined on a lattice. We introduce a class of real valued utility functions on a lattice, we show their properties in terms of comparison of lotteries and risk aversiion. The main result of the paper is a characterization of stochastic dominance through this class of functions.

     

Fuzzy density estimation

A new approach to density estimation with fuzzy random variables (FRV) is developed. In this approach, three methods (histogram, empirical c.d.f., and kernel methods) are extended for density estimation based on α-cuts of FRVs.     

Some Comments on Latent Structure Analysis, Arising Out of a Paper by R.D. Gill

The model of latent structure analysis for k discrete-valued variates was explained and illustrated by R.D. G ill [5], Here the latent structure model is related to a condition for a covariance matrix, as in factor analysis theory, and a word of warning is given for the interpretation of a factor analysis from discrete valued variables.     

A new approach to distribution free tests in contingency tables

We suggest an extremely wide class of asymptotically distribution free goodness of fit tests for testing independence in two-way contingency tables, or equivalently, independence of two discrete random variables. The nature of these tests is that the test statistics can be viewed as definite functions of the transformation of \(\widehat{T}_n = (\widehat{T}_{ij})=\Big (\frac{\nu _{ij}- n\hat{a}_i\hat{b}_j}{\sqrt{n\hat{a}_i\hat{b}_j}}\Big )\) where \(\nu _{ij}\) are frequencies and \(\hat{a}_i, \hat{b}_j\) are estimated marginal distributions. Our method is also applicable for testing independence of two discrete random vectors. We make some comparisons on statistical powers of the new tests with the conventional chi-square test and suggest some cases in which this class is significantly more powerful.     

A note on exchangeability of random variables

A sufficient condition for the induced exchangeability or partial exchangeability of linear functions of exchangeable random variables is presented. The use of this result is illustrated through the establishment of conditional exchangeability for two sets of dependent random variables that are important in constructing conditionally distribution–free test procedures for two distinctly different problems.     

Unemployment and liquidity constraints

We present a dynamic framework for the interaction between borrowing (liquidity) constraints and deviations of actual hours from desired hours, both measured by discrete‐valued indicators, and estimate it as a system of dynamic binary and ordered probit models with panel data from the Panel Study of Income Dynamics. We analyze a household's propensity to be liquidity constrained by means of a dynamic binary probit model. We analyze qualitative aspects of the conditions of employment, namely whether the household head is involuntarily overemployed, voluntarily employed, or involuntarily underemployed or unemployed, by means of a dynamic ordered probit model. We focus on the possible interaction between the two types of constraints. We estimate these models jointly using maximum simulated likelihood, where we allow for individual random effects along with an autoregressive process for the general error term in each equation. A novel feature of our method is that it allows for the random effects to be correlated with regressors in a time‐invariant fashion. Our results provide strong support for the basic theory of constrained behavior and the interaction between liquidity constraints and exogenous constraints on labor supply. Copyright © 2007 John Wiley & Sons, Ltd.     

On random fuzzy variables of second order and their application to linear statistical inference with fuzzy data

This paper summarizes some results on random fuzzy variables with existing expectation and variance, called random fuzzy variables of second order. Using the Frechét-principle and – via support functions – the embedding of convex fuzzy sets into a Banach space of functions it especially presents a unified view on expectation and variance of random fuzzy variables. These notions are applied in developing linear statistical inference with fuzzy data. Detailed investigations are presented concerning best linear unbiased estimation in linear regression models with fuzzy observations. Received: November 1999