Tables of the cumulative non-central chi-square distribution Part 51

Some bounds and approximations for the mean of second largest order sta¬tistic from an arbitrary continuous distribution symmetric about zero are obtained. For the standard normal case, FOURIER coefficients involved in the approximation procedure are tabulated and the bounds obtained are compared with other similar methods     

Tables of the cumulative non-central chi-square distribution-part 6 (finish)

The cumulative non-central chi-square distribution is tabulated for all combinations of values of α=0(0.1) 1.0 (0.2)3.0(0.5)5.0(1.0)34.0,v=1(1)30(2)50(5)100 and y=0.01 (0.01)0.1(0.1)1.0(0.2)3.0(0.5)10.0(1.0 30.0(2.0)50.0(5.0)165.0. The computations have been correctly rounded to five decimal places. Also, there is a discussion about the error involved in the computations. Furthermore, there is a discussion about possible interpolation in the table using the Lagrange's method     

Tables of the cumulative non-central cm-square distribution-part 4

The cumulative non-central chi-square .distribution is tabulated for all combinations of values of λ = 0 (0.1) 1.0 (0.2) 3.0 (0.5) 5.0 (1.0) 34.0, y=l (I) 30 (2) 50 (5) 100 and y = 0.01 (0.01) 0.1 (0.1) 1.0 (0.2) 3.0 (0.5) 10.0 (1.0 30,0 (2.0) 50,0 (5.0) 165.0. The computations have been correctly rounded to five decimal places. Also, there is a discussion about the error involved in the computations. Furthermore, there is a discussion about possible interpolation in the table using the Lagrange's method     

New representations of the noncentral chi-square density and cumulative

Representations of noncentral chi-square cumulative distribution function and probability density function are reviewed and new repre¬sentations are given. One representation of the cdf in terms of an integral is easily computed on any machine which has an accurate algorithm for computing the normal cdf.     

New modified moment estimators for the two-parameter weighted Lindley distribution

We propose a modification of the moment estimators for the two-parameter weighted Lindley distribution. The modification replaces the second sample moment (or equivalently the sample variance) by a certain sample average which is bounded on the unit interval for all values in the sample space. In this method, the estimates always exist uniquely over the entire parameter space and have consistency and asymptotic normality over the entire parameter space. The bias and mean squared error of the estimators are also examined by means of a Monte Carlo simulation study, and the empirical results show the small-sample superiority in addition to the desirable large sample properties. Monte Carlo simulation study showed that the proposed modified moment estimators have smaller biases and smaller mean-square errors than the existing moment estimators and are compared favourably with the maximum likelihood estimators in terms of bias and mean-square error. Three illustrative examples are finally presented.     

On ratio estimators in two phase sampling under size stratification and estimation over two successive occasions

Adopting size stratification when the auxiliary character is approximated by a continuous uniform distribution, a double sampling ratio strategy has been suggested. this sampling strategy has been compared with some of the known sampling strategies. the applicability of this strategy to sampling on two successive occasions has also been investigated     

Tables of cumulative distribution functions and percentiles of the standardized stable random variables

It is shown that Zolotarev's (1964) integral representation of the cumulative distribution function (c.d.f.) of stable random variables and the IMSL subroutine DCADRE (for numerical integration ) provide a natural and practically simple method for finding the values of c.d.f., the percentiles and the density function of such random variables. For symmetric stable random variables (r.v.'s ) Z_∝, values of P_∝(z) … P(0<Z_∝<z) for z … 0(.02)4.08 and ∝=.1(.2)1.9, as well as percentiles of these r.v.'s for ∝=.5(.1)2 and the percentage points .6, .7(.05).85(.025).9(.01).96(.005).995, are presented. For asymmetric stable r.v.'s we present values of their c.d.f.'s for z … 0(.1)4, ß= ?1(.25)1 and ∝=.1(.2)1.9. These result sare compared with related results of others which were obtained by using different procedure and standardization.     

An asymptotic expression for cumulative sum of probabilities of the hermite distribution

An asymptotic approximation of cumulative sum F(s) of probabilities of the Hermite distribution (Kemp C. D. and Kemp A. W. (1965)) and an asymptotic approximation of individual Hemite Probability P_s are given for large s.     

Asymptotic properties of the GMLE in the case 1 interval-censorship model with discrete inspection times

We consider the case 1 interval censorship model in which the survival time has an arbitrary distribution function F₀ and the inspection time has a discrete distribution function G. In such a model one is only able to observe the inspection time and whether the value of the survival time lies before or after the inspection time. We prove the strong consistency of the generalized maximum-likelihood estimate (GMLE) of the distribution function F₀ at the support points of G and its asymptotic normality and efficiency at what we call regular points. We also present a consistent estimate of the asymptotic variance at these points. The first result implies uniform strong consistency on [0, ∞) if F₀ is continuous and the support of G is dense in [0, ∞). For arbitrary F₀ and G, Peto (1973) and Tumbull (1976) conjectured that the convergence for the GMLE is at the usual parametric rate n½ Our asymptotic normality result supports their conjecture under our assumptions. But their conjecture was disproved by Groeneboom and Wellner (1992), who obtained the nonparametric rate ni under smoothness assumptions on the F₀ and G.     

On the asymptotic efficiency of moment and maximum likelihood estimators in the three-parameter inverse gaussian distribution

The asymptotic distribution of estimators generated by the methods of moments and maximum likelihood are considered. Simple formulae are provided which enable comparisons of asymptotic relative efficiency to be effected.     

On the asymptotic distribution of an isotonic window estimator for the generalized failure rate function

Barlow and van Zwet (1969, 1970, 1971) have proposed the isotonic window estimators for the generalized failure rate function and established some asymptotic properties. In this paper, we provide a proof, together with a set of sufficient conditions, of the asymptotic normality of an isotonic window estimator.     

A table of some percentage points of the distribution of a difference between independent chi-square variables

The distribution of Bell-Doksum measure of correlation is that of a difference between independent chi-square variables with equal weights. A table of percentage points computed here for the distribution may be used to test a hypothesis of no correlation between two variables. The distribution of a diffference between independent chi-square variables is also useful in studying variance component estimators and some general results corresponding to the distribution are given.     

On the asymptotic distribution of cramer-von mises one-sample test statistics under an alternative

The asymptotic normality of the Cramer-von Mises one-sample test statistic and one of its variants under an alternative cdf is demonstrated. The derivation herein is unique in that it does not require knowledge of the theory of weak convergence of probability measures defined on metrized function spaces, and thus is accessible to a broader class of students and practitioners.     

Asymptotic expansions of the non-central distribution of milks1 statistic in the complex gaussian case

In this paper, asymptotic expansions of the non-null distribution of the Wilks' statistic for Manova based on the complex multivariate Gaussian distribution is obtained for the cases (a) when the non-centrality parameter ω_～is constant and (b) when _～Is of the same order as the sample size. No direct method is available for the case (b) and thus the partial differential equation approach considered in this paper Is particularly useful     

On the distribution of a new measure of heaviness of tails

Distributional properties are given for a statistic T*, which has previously been reported to have power properties as a test of normality as attractive as those of the sample kurtosis or perhaps slightly more attractive. Asymptotic results, the mean and variance under normality, the range of variation, and approximation of critical values for testing normality are obtained     

On the asymptotic distribution of the Dickey Fuller-GLS test statistic

In a very influential paper, Elliott et al. [Efficient tests for an autoregressive unit root. Econometrica. 1996;64:813–836] show that no uniformly most powerful test for the unit root testing problem exits, derive the relevant power envelope and characterize a family of point-optimal tests. As a by-product, they also propose a ‘generalized least squares (GLS) detrended’ version of the conventional Dickey–Fuller test, denoted DF-GLS, that has since then become very popular among practitioners, much more so than the point-optimal tests. In view of this, it is quite strange to find that, while conjectured in Elliott et al. [Efficient tests for an autoregressive unit root. Econometrica. 1996;64:813–836], so far there seems to be no formal proof of the asymptotic distribution of the DF-GLS test statistic. By providing three separate proofs, the current paper not only substantiates the required result, but also provides insight regarding the pros and cons of different methods of proof.     

Tables of confidence limits on the tail area of the normal distribution

Tables are given of confidence limits on tail areas, γ, of the normal distribution, where γ = P{Y ≥ L}, and where L is a given number, and Y is normally distributed with unknown mean, μ, and unknown variance, σ².     

Further asymptotic expansions of the distribution of the sphericity test

In this paper new asymptotic expansions of the distributions of the sphericity test criterion are obtained in the null and the non-null case when the alternatives are close to the hypothesis. These expansions are obtained for the first time in terms of beta distributions. These appear to be better than the ones available in the literature.     

Estimating the probability of obtaining nonfeasible parameter estimates of the generalized pareto distribution

In this paper we consider the problem of estimating the parameters of the generalized Pareto distribution. Both the method of moments and probability-weighted moments do not guarantee that their respective estimates will be consistent with the observed data. We present simple programs to predict the probability of obtaining such nonfeasible estimates. Our estimation techniques are based on results from intensive simulations and the successful modelling of the lower tail of the distribution of the upper bound of the support. More simulations are performed to validate the new procedure.