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Nickos Papadatos

Publications and source records attributed to Nickos Papadatos.

18 recordsLinked to original sources

On corrected Poisson approximations for sums of independent indicators

Let $S_n=I_1+\cdots+I_n$ be a sum of independent indicators $I_i$, with $p_i=\Pr(I_i=1)=1-\Pr(I_i=0)$, $i=1,\ldots,n$. It is well-known that the total variation distance between $S_n$ and $Z_λ$, where $Z_λ$ has a Poisson distribution with mean $λ=\sum_{i=1}^n p_i$, is typically of order $\sum_{i=1}^n p_i^2$. In the present work we propose a class of corrected Poisson approximations, which enable the second order factorial moment distance (and hence, the total variation distance) to be bounded above by a constant multiple of $\sum_{i=1}^n p_i^3$ and $\sum_{i=1}^n p_i^4$, hence improving the order of approximation.

math.PR

A discrete analogue of Terrell's characterization of rectangular distributions

George R. Terrell (1983, {Ann. Probab., vol. 11(3), pp. 823--826) showed that the Pearson coefficient of correlation of an ordered pair from a random sample of size two is at most one-half, and the equality is attained only for rectangular (uniform over some interval) distributions. In the present note it is proved that the same is true for the discrete case, in the sense that the correlation coefficient attains its maximal value only for discrete rectangular (uniform over some finite lattice) distributions. MSC: Primary 60E15; 62E10; Secondary 62G30. Key words and phrases: discrete rectangular distribution; order statistics; Hahn polynomials; Pearson coefficient of correlation.

math.PR

On point estimators for Gamma and Beta distributions

Let $X_1,\ldots,X_n$ be a random sample from the Gamma distribution with density $f(x)=λ^αx^{α-1}e^{-λx}/Γ(α)$, $x>0$, where both $α>0$ (the shape parameter) and $λ>0$ (the reciprocal scale parameter) are unknown. The main result shows that the uniformly minimum variance unbiased estimator (UMVUE) of the shape parameter, $α$, exists if and only if $n\geq 4$; moreover, it has finite variance if and only if $n\geq 6$. More precisely, the form of the UMVUE is given for all parametric functions $α$, $λ$, $1/α$ and $1/λ$. Furthermore, a highly efficient estimating procedure for the two-parameter Beta distribution is also given. This is based on a Stein-type covariance identity for the Beta distribution, followed by an application of the theory of $U$-statistics and the delta-method. MSC: Primary 62F10; 62F12; Secondary 62E15. Key words and phrases: unbiased estimation; Gamma distribution; Beta distribution; Ye-Chen-type closed-form estimators; asymptotic efficiency; $U$-statistics; Stein-type covariance identity; delta-method.

math.ST

On the inversion of the Laplace transform (In Memory of Dimitris Gatzouras)

The Laplace transform is a useful and powerful analytic tool with applications to several areas of applied mathematics, including differential equations, probability and statistics. Similarly to the inversion of the Fourier transform, inversion formulae for the Laplace transform are of central importance; such formulae are old and well-known (Fourier-Mellin or Bromwich integral, Post-Widder inversion). The present work is motivated from an elementary statistical problem, namely, the unbiased estimation of a parametric function of the scale in the basic model of a random sample from exponential distribution. The form of the uniformly minimum variance unbiased estimator of a parametric function $h(λ)$, as well as its variance, are obtained as series in Laguerre polynomials and the corresponding Fourier coefficients, and a particular application of this result yields a novel inversion formula for the Laplace transform. MSC: Primary 44A10, 62F10. Key words and phrases: Exponential Distribution, Unbiased Estimation; Fourier-Laguerre Series; Inverse Laplace Transform; Laguerre Polynomials.

math.PR

The characteristic function of the discrete Cauchy distribution (In Memory of T. Cacoullos)

A new family of integer-valued Cauchy-type distributions is introduced, the {\it Cauchy-Cacoullos family}. The characteristic function is evaluated, showing some interesting distributional properties, similar to the ordinary (continuous) Cauchy scale family. The results are extendable to discrete Student-type distributions with odd degrees of freedom. Keywords: Fourier series; discrete Student distribution; Cauchy-Cacoullos family.

math.PR

Sequences of expected record values

We investigate conditions in order to decide whether a given sequence of real numbers represents expected record values arising from an independent, identically distributed, sequence of random variables. The main result provides a necessary and sufficient condition, relating any expected record sequence with the Stieltjes moment problem. The results are proved by means of a useful transformation on random variables. Some properties of this mapping, and its inverse, are discussed in detail, and, under mild conditions, an explicit inversion formula for the random variable that admits a given expected record sequence is obtained. Key words and phrases: characterizations; expected record values; Stieltjes moment problem; transformation of random variables; inversion formula. AMS subject classification: Primary 60E05, 62G30; Secondary 44A60.

math.PR

Optimal moment inequalities for order statistics from nonnegative random variables

We obtain the best possible upper bounds for the moments of a single order statistic from independent, non-negative random variables, in terms of the population mean. The main result covers the independent identically distributed case. Furthermore, the case of the sample minimum for merely independent (not necessarily identically distributed) random variables is treated in detail. Key-words and phrases: order statistics; optimal moment bounds; nonnegative random variables; sample minimum; reliability systems.

math.ST

Orthogonal polynomials in the Cumulative Ord family and its application to variance bounds

This article presents and reviews several basic properties of the Cumulative Ord family of distributions; this family contains all the commonly used discrete distributions. A complete classification of the Ord family of probability mass functions is related to the orthogonality of the corresponding Rodrigues polynomials. Also, for any random variable $X$ of this family and for any suitable function $g$ in $L^2(\mathbb{R},X)$, the article provides useful relationships between the Fourier coefficients of $g$ (with respect to the orthonormal polynomial system associated to $X$) and the Fourier coefficients of the forward difference of $g$ (with respect to another system of polynomials, orthonormal with respect to another distribution of the system). Finally, using these properties, a class of bounds for the variance of $g(X)$ is obtained, in terms of the forward differences of $g$. These bounds unify and improve several existing results.

math.PR

On the limiting distribution of sample central moments

We investigate the limiting behavior of sample central moments, examining the special cases where the limiting (as the sample size tends to infinity) distribution is degenerate. Parent (non-degenerate) distributions with this property are called \emph{singular}, and we show in this article that the singular distributions contain at most three supporting points. Moreover, using the \emph{delta}-method, we show that the (second order) limiting distribution of sample central moments from a singular distribution is either a multiple, or a difference of two multiples of independent chi-square random variables with one degree of freedom. Finally, we present a new characterization of normality through the asymptotic independence of the sample mean and all sample central moments.

math.ST

On sequences of expected maxima and expected ranges

We investigate conditions in order to decide whether a given sequence of real numbers represents expected maxima or expected ranges. The main result provides a novel necessary and sufficient condition, relating an expected maxima sequence to a translation of a Bernstein function through its Lévy-Khintchine representation. Key words and phrases: expected maxima; expected ranges; Bernstein functions, Lévy-Khintchine representation, order statistics.

stat.ME

Maximizing the expected range from dependent observations under mean-variance information

In this article we derive the best possible upper bound for $E[\max{X_i}-\min_i{X_i}]$ under given means and variances on $n$ random variables $X_i$. The random vector $(X_1,...,X_n)$ is allowed to have any dependence structure, provided $E X_i=μ_i$ and $Var X_i=σ_i^2$, $0<σ_i<\infty$. We provide an explicit characterization of the $n$-variate distributions that attain the equality (extremal random vectors), and the tight bound is compared to other existing results. Key words and phrases: Range; Dependent Observations; Tight Expectation Bounds; Extremal Random Vectors; Probability Matrices; Characterizations.

stat.ME

A Simple Method for Obtaining the Maximal Correlation Coefficient and Related Characterizations

We provide a method that enables the simple calculation of the maximal correlation coefficient of a bivariate distribution, under suitable conditions. In particular, the method readily applies to known results on order statistics and records. As an application we provide a new characterization of the exponential distribution: Under a splitting model on independent identically distributed observations, it is the (unique, up to a location-scale transformation) parent distribution that maximizes the correlation coefficient between the records among two different branches of the splitting sequence.

stat.ME

Integrated Pearson family and orthogonality of the Rodrigues polynomials: A review including new results and an alternative classification of the Pearson system

An alternative classification of the Pearson family of probability densities is related to the orthogonality of the corresponding Rodrigues polynomials. This leads to a subset of the ordinary Pearson system, the Integrated Pearson Family. Basic properties of this family are discussed and reviewed, and some new results are presented. A detailed comparison between the integrated Pearson family and the ordinary Pearson system is presented, including an algorithm that enables to decide whether a given Pearson density belongs to the integrated system, or not. Recurrences between the derivatives of the corresponding orthonormal polynomial systems are also given.

stat.ME

Some Counterexamples Concerning Maximal Correlation and Linear Regression

A class of examples concerning the relationship of linear regression and maximal correlation is provided. More precisely, these examples show that if two random variables have (strictly) linear regression on each other, then their maximal correlation is not necessarily equal to their (absolute) correlation.

math.ST

Self-Inverse and Exchangeable Random Variables

A random variable Z will be called self-inverse if it has the same distribution as its reciprocal 1/Z. It is shown that if Z is defined as a ratio, X/Y, of two rv's X and Y (with Pr[X=0]=Pr[Y=0]=0), then Z is self-inverse if and only if X and Y are (or can be chosen to be) exchangeable. In general, however, there may not exist iid X and Y in the ratio representation of Z.

stat.ME

Linear Estimation of Location and Scale Parameters Using Partial Maxima

Consider an i.i.d. sample X^*_1,X^*_2,...,X^*_n from a location-scale family, and assume that the only available observations consist of the partial maxima (or minima)sequence, X^*_{1:1},X^*_{2:2},...,X^*_{n:n}, where X^*_{j:j}=max{X^*_1,...,X^*_j}. This kind of truncation appears in several circumstances, including best performances in athletics events. In the case of partial maxima, the form of the BLUEs (best linear unbiased estimators) is quite similar to the form of the well-known Lloyd's (1952, Least-squares estimation of location and scale parameters using order statistics, Biometrika, vol. 39, pp. 88-95) BLUEs, based on (the sufficient sample of) order statistics, but, in contrast to the classical case, their consistency is no longer obvious. The present paper is mainly concerned with the scale parameter, showing that the variance of the partial maxima BLUE is at most of order O(1/log n), for a wide class of distributions.

math.ST