A general approximation to quantiles

For many continuous distributions, a closed-form expression for their quantiles does not exist. Numerical approximations for their quantiles are developed on a distribution-by-distribution basis. This work develops a general approximation for quantiles using the Taylor expansion. Our method only requires that the distribution has a continuous probability density function and its derivatives can be derived to a certain order (usually 3 or 4). We demonstrate our unified approach by approximating the quantiles of the normal, exponential, and chi-square distributions. The approximation works well for these distributions.     

Study of the quality of several asymptotic and near-exact approximations based on moments for the distribution of the Wilks Lambda statistic

In this paper a measure of proximity of distributions, when moments are known, is proposed. Based on cases where the exact distribution is known, evidence is given that the proposed measure is accurate to evaluate the proximity of quantiles (exact vs. approximated). The measure may be applied to compare asymptotic and near-exact approximations to distributions, in situations where although being known the exact moments, the exact distribution is not known or the expression for its probability density function is not known or too complicated to handle. In this paper the measure is applied to compare newly proposed asymptotic and near-exact approximations to the distribution of the Wilks Lambda statistic when both groups of variables have an odd number of variables. This measure is also applied to the study of several cases of telescopic near-exact approximations to the exact distribution of the Wilks Lambda statistic based on mixtures of generalized near-integer gamma distributions.     

Exact and approximate distributions for the product of inverted Dirichlet components

It is well known that X/(X+Y) has the beta distribution when X and Y follow the inverted Dirichlet distribution. In this paper, we derive the exact distribution of the product P=XY (involving the Gauss hypergeometric function) and the corresponding moment properties. We also propose two approximations and show evidence of their goodness of fit. The work is motivated by real-life examples.     

Saddlepoint Approximation for Sample Quantiles with Some Applications

In this article, we use the integral form of the binomial distribution to derive saddlepoint approximations for sample quantiles. As an application, we present the calculation of the tail probability of the empirical log-likelihood ratio statistic for quantiles. Simulation results are also given to show that our approximations are extremely accurate.     

Near-exact distributions for the sphericity likelihood ratio test statistic

In this paper three near-exact distributions are developed for the sphericity test statistic. The exact probability density function of this statistic is usually represented through the use of the Meijer G function, which renders the computation of quantiles impossible even for a moderately large number of variables. The main purpose of this paper is to obtain near-exact distributions that lie closer to the exact distribution than the asymptotic distributions while, at the same time, correspond to density and cumulative distribution functions practical to use, allowing for an easy determination of quantiles. In addition to this, two asymptotic distributions that lie closer to the exact distribution than the existing ones were developed. Two measures are considered to evaluate the proximity between the exact and the asymptotic and near-exact distributions developed. As a reference we use the saddlepoint approximations developed by Butler et al. [1993. Saddlepoint approximations for tests of block independence, sphericity and equal variances and covariances. J. Roy. Statist. Soc., Ser. B 55, 171–183] as well as the asymptotic distribution proposed by Box.     

Approximations of distributions for some standardized partial sums in sequential analysis

In sequential analysis it is often necessary to determine the distributions of √t Y _t and/or √a Y _t , where t is a stopping time of the form t = inf{ n ≥ 1 : n+S_n+ξ_n> a }, Y _n is the sample mean of n independent and identically distributed random variables (iidrvs) Y_i with mean zero and variance one, S_n is the partial sum of iidrvs X_i with mean zero and a positive finite variance, and { ξ_n } is a sequence of random variables that converges in distribution to a random variable ξ as n →∞ and ξ_n is independent of ( X_n+1, Y_n+1), (X_n+2, Y_n+2), . . . for all n ≥ 1. Anscombe's (1952) central limit theorem asserts that both √t Y _t and √a Y _t are asymptotically normal for large a , but a normal approximation is not accurate enough for many applications. Refined approximations are available only for a few special cases of the general setting above and are often very complex. This paper provides some simple Edgeworth approximations that are numerically satisfactory for the problems it considers.     

Characterization of hypergeometric type distributions by regression

The conditional distribution of Y given X is proved to be of the positive or the negative hypergeometric law when X follows a positive or a negative hypergeometric distribution, respectively and the regression function of X on Y is of a known linear form     

COMPARISON OF FEATURE SIGNIFICANCE QUANTILE APPROXIMATIONS

Curve estimates and surface estimates often contain features such as inclines, bumps or ridges which may signify an underlying structural mechanism. However, spurious features are also a common occurrence and it is important to identify those features that are statistically significant. A method has been developed recently for recognising feature significance based on the derivatives of the function estimate. It requires simultaneous confidence intervals and tests, which in turn require quantiles for the maximal deviation statistics. This paper reviews and compares various approximations to these quantiles. Applying upcrossing‐probability theory to this problem yields better quantile approximations than the use of an independent blocks method.     

Test of Association Between Two Ordinal Variables While Adjusting for Covariates

We propose a new set of test statistics to examine the association between two ordinal categorical variables X and Y after adjusting for continuous and/or categorical covariates Z. Our approach first fits multinomial (e.g., proportional odds) models of X and Y, separately, on Z. For each subject, we then compute the conditional distributions of X and Y given Z. If there is no relationship between X and Y after adjusting for Z, then these conditional distributions will be independent, and the observed value of (X, Y) for a subject is expected to follow the product distribution of these conditional distributions. We consider two simple ways of testing the null of conditional independence, both of which treat X and Y equally, in the sense that they do not require specifying an outcome and a predictor variable. The first approach adds these product distributions across all subjects to obtain the expected distribution of (X, Y) under the null and then contrasts it with the observed unconditional distribution of (X, Y). Our second approach computes "residuals" from the two multinomial models and then tests for correlation between these residuals; we define a new individual-level residual for models with ordinal outcomes. We present methods for computing p-values using either the empirical or asymptotic distributions of our test statistics. Through simulations, we demonstrate that our test statistics perform well in terms of power and Type I error rate when compared to proportional odds models which treat X as either a continuous or categorical predictor. We apply our methods to data from a study of visual impairment in children and to a study of cervical abnormalities in human immunodeficiency virus (HIV)-infected women. Supplemental materials for the article are available online.     

On Censored Bivariate Random Variables: Copula,Characterization, and Estimation

Let (X, Y) be a bivariate random vector with joint distribution function F_{X, Y}(x, y) = C(F(x), G(y)), where C is a copula and F and G are marginal distributions of X and Y, respectively. Suppose that (X_i, Y_i), i = 1, 2, …, n is a random sample from (X, Y) but we are able to observe only the data consisting of those pairs (X_i, Y_i) for which X_i ? Y_i. We denote such pairs as (X*_i, Y_i*), i = 1, 2, …, ν, where ν is a random variable. The main problem of interest is to express the distribution function F_{X, Y}(x, y) and marginal distributions F and G with the distribution function of observed random variables X* and Y*. It is shown that if X and Y are exchangeable with marginal distribution function F, then F can be uniquely determined by the distributions of X* and Y*. It is also shown that if X and Y are independent and absolutely continuous, then F and G can be expressed through the distribution functions of X* and Y* and the stress–strength reliability P{X ? Y}. This allows also to estimate P{X ? Y} with the truncated observations (X*_i, Y_i*). The copula of bivariate random vector (X*, Y*) is also derived.     

Development and comparative study of two near-exact approximations to the distribution of the product of an odd number of independent Beta random variables

Using the concept of near-exact approximation to a distribution we developed two different near-exact approximations to the distribution of the product of an odd number of particular independent Beta random variables (r.v.'s). One of them is a particular generalized near-integer Gamma (GNIG) distribution and the other is a mixture of two GNIG distributions. These near-exact distributions are mostly adequate to be used as a basis for approximations of distributions of several statistics used in multivariate analysis. By factoring the characteristic function (c.f.) of the logarithm of the product of the Beta r.v.'s, and then replacing a suitably chosen factor of that c.f. by an adequate asymptotic result it is possible to obtain what we call a near-exact c.f., which gives rise to the near-exact approximation to the exact distribution. Depending on the asymptotic result used to replace the chosen parts of the c.f., one may obtain different near-exact approximations. Moments from the two near-exact approximations developed are compared with the exact ones. The two approximations are also compared with each other, namely in terms of moments and quantiles.     

Computation of the central and noncentral f distributions

Recursion relations suitable for rapid computation are derived for the cumulative distribution of F′ = (X/m)/(Y/n) where X is χ²(λ, m) and Y is independently χ²(n). When n is even no complicated function evaluations are needed. For n odd, a special doubly noncentral t distribution is needed to start the computation. Series representations for this t distribution are given with rigorous bounds on truncation errors. Proper recursion techniques for numerical evaluation of the special functions are given.     

Estimating the distribution function of a transformed random vector

Frequently a random vector Y with known distribution function is readily observed. However, the random variable of interest is a transformation of Y say h(Y), and sample values of h are expensive to evaluate. The objective is to estimate the distribution function of using only a small sample on Y. Four estimators are proposed for use when Y is discrete. A Monte Carlo study of the estimators is presented This estimation problem frequently arises when Y is a parameter in a mathematical programming problem and h(Y) is the optimal objective function value. Two examples of this type are presented.     

Computation of the Generalized F Distribution

Exact expressions for the cumulative distribution function of a random variable of the form ( α ₁ X ₁+ α ₂ X ₂)/ Y are given where X ₁, X ₂ and Y are independent chi-squared random variables. The expressions are applied to the detection of joint outliers and Hotelling's mis-specified T ² distribution.     

Saddlepoint Approximations for the Difference of Order Statistics and Studentized Sample Quantiles

For a sample from a given distribution the difference of two order statistics and the Studentized quantile are statistics whose distribution is needed to obtain tests and confidence intervals for quantiles and quantile differences. This paper gives saddlepoint approximations for densities and saddlepoint approximations of the Lugannani–Rice form for tail probabilities of these statistics. The relative errors of the approximations are n ⁻¹ uniformly in a neighbourhood of the parameters and this uniformity is global if the densities are log-concave.     

The likelihood ratio test for simple tree order: A useful asymptotic expansion

We derive new approximations for the likelihood ratio statistics that are used in testing hypotheses involving simple tree order. They are based on asymptotics when the number of populations tends to infinity and variances are equal.As an application quantiles are obtained that are much easier to calculate than exact critical values or the gamma-approximation usually proposed in literature. Our quantitles also provide often better approximations than those known in literature.     

Expressions for the distribution and percentiles of the sums and products of chi-squares

Linear combinations of central or non-central chi-squares occur naturally in a variety of contexts. The products of chi-squares occur when a variance has a chi-square prior and in electrical engineering. Here, we give expansions for their distribution and quantiles and also for the products of the powers of chi-squares, including ratios. These provide much more accurate approximations than those based on asymptotic normality. The larger the degrees of freedom or the larger the non-centrality parameters, the better the approximations. We give the first four terms of these expansions. These provide approximations with errors smaller by five magnitudes than those based on asymptotic normality or on Satterthwaite's approximation. His method matched the first two moments of the target and a multiple of a chi-square and is only a first-order approximation like that based on the central limit theorem. We show that it can be made second order by matching the first three moments. The appendices show how to obtain analytical expressions for the distribution of weighted sums of chi-squares.     

On some properties of bivariate weighted distributions

Let X^w and Y^w be weighted random variables arising from the distribution of (X,Y). We explore implications of independence of X and Y on the dependence structure of (X^w, Y^w). We also show that when X and Y are independent and the weight function is symmetric, identical distribution of X^w and Y^w implies that of X and Y. We discuss application of these results to the study of a renewal process.     

Using power transformations when approximating quantiles

Estimators are obtained tor quantiles of survival distributions. This is accomplished by approximating Lritr distribution of the transtorrneri data, where the transformation used is that of Box and Cox (1964). The normal approximation as in Box and Cox and, in addition, the extreme value approximation are considered. More generally, to use the methods given, the approximating distribution must come from a location-scale family. For some commonly used survival random variables T the performance of the above approximations are evaluated in terms of the ratio of the true quantiles of T to the estimated one, in the long run. This performance is also evaluated for lower quantiles using simulated lognormai, Weibull and gamma data. Several examples are given to illustrate the methodology herein, including one with actual data.     

Joint analysis of longitudinal data comprising repeated measures and times to events

In biomedical and public health research, both repeated measures of biomarkers Y as well as times T to key clinical events are often collected for a subject. The scientific question is how the distribution of the responses [ T , Y | X ] changes with covariates X . [ T | X ] may be the focus of the estimation where Y can be used as a surrogate for T . Alternatively, T may be the time to drop-out in a study in which [ Y | X ] is the target for estimation. Also, the focus of a study might be on the effects of covariates X on both T and Y or on some underlying latent variable which is thought to be manifested in the observable outcomes. In this paper, we present a general model for the joint analysis of [ T , Y | X ] and apply the model to estimate [ T | X ] and other related functionals by using the relevant information in both T and Y . We adopt a latent variable formulation like that of Fawcett and Thomas and use it to estimate several quantities of clinical relevance to determine the efficacy of a treatment in a clinical trial setting. We use a Markov chain Monte Carlo algorithm to estimate the model's parameters. We illustrate the methodology with an analysis of data from a clinical trial comparing risperidone with a placebo for the treatment of schizophrenia.