Fourier inversion—distribution functions—long tails

Abstract

Given a characteristic functionφ(t) we want to calculate the corresponding distribution function. For the sake of simplicity we will assume that the mean value of the distribution is zero, i.e. that φ'(0) =0. For these calculations we will use the following formula where The dash on the summation sign indicates that the term corresponding to k = 0 is missing.     

The use of efficient estimates in multiple tests for goodness of fit

Abstract

Let be Pearson's statistics for testing goodness of fit in various marginal distributions associated with a categorized array of N objects. This study is concerned with disturbances in the limiting joint distribution of when maximum likelihood estimates from the original ungrouped data are used instead of the usual estimates from the cell frequencies after grouping. Under regularity conditions the limiting distributions of , and are shown to satisfy for each positive {c^b1 x ... x c^bT }, where A(c) is the Cartesian product set A(c) = (0, c^b1 ] x ... x (0, c^bT ]. The limiting distributions are characterized in terms of partitioned Wishart matrices having unit rank and parameters as appropriate. These results are extensions of work by Chernoff and Lehmann (1954) and Jensen (1974).     

On the ω test of dependence between statistical variables

Abstract

1. In an earlier Note¹ I have suggested to measure the dependence between statistical variables by the expression where p_ij is the probability that x assumes the value x_i and y the value y_j , while By is meant summation with respect to all i and j for which p_ij > p_i* p_*j .     

On asymptotic properties of an estimate of a functional of a probability density

Abstract

Bhattacharyya & Roussas (1969) proposed to estimate the functional Δ = ∫ _?∞/^∞ f ²(x)dx by , where is a kernel estimate of the probability density f(x). Schuster (1974) proposed, alternatively, to estimate Δ by , where F _n (x) is the sample distribution function, and showed that the two estimates attain the same rate of strong convergence to Δ. In this note, two large sample properties of are presented, first strong convergence of to Δ is established under less assumptions than those of Schuster (1974), and second the asymptotic normality of established.     

Contribution to the study of grouped observation. II. Loss of information caused by grouping of normally distributed observations

Abstract

1. Introduction

(a) Maximum Likelihood.—In a previous paper (THIS JOURNAL, vol. XXXII, 1949, pp. 135–159) the author gave tables of the functions and where ?(x) denotes the normal law of distribution, φ(x) its integral and ?′(x) its first derivative. With the aid of these tables it is practicable to solve the maximum likelihood equations for coarsely grouped normal observations. The procedure was illustrated by examples.     

Truncated inverse binomial sampling

Abstract

1.Introductions Inverse Binomial Sampling.

From an infinite population of a's and b's, in proportions p and q = 1-p respectively, individuals are drawn at random until M a's are found. Thus the sample size, n, is a random variable. Its well-known distribution is      

On the test of the hypothesis that the probability of an event is contained within given limits

Asbtract

The hypernormal (or Lexian) frequency function can be defined by the integral where θ(p) is the frequency (or density) function of p defined in the interval. We have, of course, that and that .     

On the remainder in the central limit theorem

Abstract

Let X^bv (v = 1,2, ..., n) be independent random variables with the distribution functions F_bvx) and suppose . We define a random variable by where and denote the distribution function of X by F (x.     

Consistent estimators of the parameters of a linear structural relation

Abstract

1. Summary of results. Let E and Eo be chance variables at least one of which is not normally distributed (throughout the present paper a chance variable which is constant with probability one will be considered to be normally distributed with variance zero), and whose distribution is otherwise unknown, except that it is known that with probability one, where 0 and p are unknown constants, . Let (u; v) be jointly normally distributed chance variables with unknown covariance matrix, distributed independently of (ε, ε₀). Without loss of generality we assume that the expected values E u and E v, of u and v respectively, are both zero. Define      

Short note: Stein's two-stage procedure for the intra-class model

In his nice paper (Mykhopadhyay, 1982) as well as in his significant monograph (Mykhopadhyay & Solanky, 1994) N. Mykhopadhyay considers the following application of STEIN's two-stage procedure: Suppose that (X ₁,..., X_n ) ^T , n = 1, 2,..., is n-dimensional normal with mean vector µ = µ l and dispersion matrix Σ _n =σ ²(ρ_ij ) with ρ_ij = 1, ρ_ij = ρ ^*, i ≠ j = 1,..., n where (µ, Σ, ρ) ∈ ? × ?⁺ × (-1, 0); this is called the intra-class model. For given d > 0 and α ∈ (0, 1) one wants to construct a (sequential) confidence interval I for µ having width 2d and confidence coefficient at least (1 - α). It is claimed that where N is determined, according to Stein's two-stage procedure (Stein, 1945), as where m ? 2 is the first stage sample size and denotes the sample variance, fulfills this aim.     

On an analogue of Cramér-Rao's inequality

Abstract

Rao [1] and simultaneously Cramér [2, 3] have shown that if f (x, θ) is the probability density function of a distribution involving an unknown parameter θ and distributed over the range α ? x ? b, where a and b are independent of θ, and if x ₁ x ₂ ... x _n is a random sample of n independent observations from this distribution, the variance of any estimate unbiased for Ψ (θ), satisfies the inequality where E denotes mathematical expectation and is Fisher's information index about θ. In (1), equality holds if, and only if, θ* is sufficient for θ. This inequality is further generalized to the multi-parametric case.     

Programme of the Tenth International Congress of Actuaries

Abstract

The problem of “optimum stratification” was discussed by the firstmentioned author in an earlier paper (1). The discussion in that paper was limited to sampling from an infinite population, represented by a density function f{y). The optimum points y_i of stratification, for estimating the mean µ using were determined by solving the equations: which gives the stratification points Y_i that minimize the sampling variance V _y (provided the usual condition for the minimum is fulfilled)     

Eksamen i Statistik ved Københavns Universitet

Abstract

Extract

d1. Bestern karakteristikkerne for den partielle differentialigning Gør rede for, at der ved begyndelsebetingelsen z=2√x for 0<x<+∞,y=0 fastlægges netop en løsning til (*) i et passende område ω i xy-planen, og bestem denne løsning (herunder et brugbart område ω).     

A moment inequality with an application to the central limit theorem

Abstract

Consider a sequence of independent random variables (r.v.) X ₁ X ₂, …, X_n , … , with the same distribution function (d.f.) F(x). Let E (X_n ) = 0, _E , E (?(X)) denoting the mean value of the r.v. ? (X). Further, let the r.v. where have the d.f. F _n (x). It was proved by Berry [1] and the present author (Esseen [2], [4]) that Φ(x) being the normal d.f.      

Über die Bestimmung des systematischen Fehlers,den man bei der Schätzung gewisser Funktionen apriorischer Wahrscheinlichkeiten aus empirischen relativen Häufigkeiten begeht,mittels einer unendlichen Reihe

Abstract

A. A. Tschuprow hat in seinen beiden Schriften: Grundbegriffe und Grundprobleme der Korrelationstheorie. Leipzig und Berlin 1925. (zitiert als A) und On the mathematical expectation of the moments of frequency distributions. Biometrica Bd. XII. (zitiert als B) die Grundlagen der Theorie der systematischen Fehler in der Wahrscheinlichkeitslehre im Zusammenhang entwickelt. Von ihm wird stets das Schema der Ziehungen aus einer Urne mit pk weissen und qk roten Kugeln (p + q= 1) zugrunde gelegt. Dann ist die Wahrscheinlichkeit, mit N Zügen mit Zurücklegen r weisse Kugeln zu ziehen:      

Zur Kritik der Ersatzmethoden der direkten Bestimmung von Erbzahlen bei nicht repräsentativem Material

Abstract

Für die Untersuchung auf bestimmte Erbzahlen bei unvollständigem einseitig ausgelesenem Material gibt es zwei Methoden : 1. den Vergleich der in dem un vollständigen Material gefundenen Häufigkeit bestimmter Merkmalträger mit der Erwartung auf Grund der Annahme einer bestimmten Erbregel , wobei k die Grösse der Sippschaften, p die erwartete Erbzahl und q = 1 ? p ist. Diese Formel ist von mir schon 1912 aufgestellt. Bernstein nennt diese Methode nicht sehr glücklich die apriorische. Erstmals ist sie 1916 praktisch von Apert angewandt. Man wird sie besser als direkte Vergleichsmethode bezeichnen.

2. Die Feststellung der Häufigkeit s des Merkmals bei den Gesehwistern seiner Träger, T. Ist deren Zahl t, k die Sippschaftsgrösse, t_x die Zahl der Träger mit x Trägergeschwisten, so ergibt sich theoretisch .

     

Analytical studies in stop-loss reinsurance. II

Abstract

I

In an earlier paper [5] we discussed the problem of finding an unbiased estimator of where p (x, 0) is a given frequency density and 0 is a (set of) parameter(s). In general, will not be an unbiased estimator of (1), when Ô is an unbiased estimate of O. In [5] it was shown that is an unbiased estimator of (1), if we define y_i , as the larger of 0 and X _j - c. It was emphasized that the resulting estimate may very well be zero, even when it is unreasonable to assume that the premium for a stop.loss reinsurance. defined by a frequency p (x, 0) of claims x and a critical limit c, should be zero when the critical limit has not been exceeded during the n years considered for the determination of the premium.     

Aktuareksamen og Eksamen i Statistik ved Københavns Universitet

Abstract

Extract

d1. Vis, at man for n ? 2 har når x ikke antager nogen af værdierne 0, ?1, ..., ?n+1, og når x ikke antager nogen af værdierne 0, 1, ..., n+1.     

Sur le calcul numérique des moments ordinaires et des moments composés d'une distribution statistique

Abstract

Considérons une distribution statistique (empirique ou théorique) x _v (v = 1, 2 ... n) désignant les valeurs que peut assumer une variable fortuite une-dimensionelle x, et P_v (v= 1,2 ... n) désignant les fréquences observées (absolues ou relatives) ou bien les probabilités des valeurs x_v .     

A note on the behaviour of the “tail” of the mixture-poisson distribution

Abstract

A mixture-Poisson distribution is defined by where U(x) is a distribution function concentrated on (0, ∞). This distribution has been applied as a model of the number of claims occurring in an insurance business during a certain period of time.