ASYMPTOTIC EXPANSIONS APPLIED TO F-TEST CRITERIA

Using asymptotic expansions of the Kummer hypergeometric function, the sequential. F-test criterion is evaluated asymptotically in terms of the sample size. The continuation region inequalities for the test are inverted and expressed in terms of known test criteria. A rapidly converging algorithm for carrying out the sequential procedure is provided. This makes the F-test easier for the practitioner to use. Almost sure finite termination of the sequential. F-test is asserted by appealing to the continuation inequalities and a heuristic asymptotic expansion of the test criterion. Average stopping times of the sequential procedure for a variety of population means and population number configurations are tabulated. The computer symbolic manipulation program MAPLE was used to derive some formulae.     

BALANCED DESIGNS FOR ROW-AND-COLUMN EXPERIMENTS WITH TWO NON-INTERACTING SETS OF TREATMENTS, ONE SET NOT BEING APPLIED TO ALL THE ROWS1

This note gives and discusses balanced row-and-column designs for experiments with two non-interacting sets of treatments, one set not being applied to all the rows. These designs are potentially useful for occasions when trees that have tested one set of experimental treatments are needed for testing a second set before the residual effects of the first have become negligible, but when the experimenter wishes to apply the second set to only some of the rows. The designs are appropriate if the residual and new effects do not interact.     

TESTING FOR INDEPEDENCE IN MULTIVARIATE EXPONENTIAL DISTRIBUTIONS2

In this paper the work of Weier & Basu (1980) is extended to a special case of the trivariate exponential distributions and to the general k-variate case. In the trivariate case several statistics are derived including one based on the likelihood ratio approach and the locally most powerful rank statistic, and power studies are carried out. The general k-variate model is derived, and testing for independence is shown to reduce to a solved problem.     

ON POINT MULTISERIAL CORRELATION1

Abstract
We present a simple form for the estimator of the point multiserial correlation coefficient between a quantitative variate X and a qualitative variate 7. Given a bivariate sample grouped in the form of an r × c contingency table the estimator is based on finding the optimum Y -scores which maximize the correlation coefficient. The resulting estimator is equivalent to Das Gupta's (1960) for ungrouped X -values, with the advantage of simplicity in its calculation. Under the assumption of conditional normality, the significance of point multiserial correlation may be studied by an F -test.     

A PROBABILISTIC DERIVATION OF THE NON-CENTRAL χ2 DISTRIBUTION

A simple derivation of the non-central χ² distribution is presented. This requires no advanced mathematical knowledge, and is suitable for use in elementary courses.
The non-central χ² distribution is of great importance in statistical theory, both in its own right, and as a step in the derivation of other distributions; however, it tends to be neglected in statistical courses largely, it seems, because the standard derivations are too difficult. A recent review of derivations by Guenther (1964) shows that most require some knowledge of n -dimensional geometry or the equivalent matrix theory. The alternative is the use of generating functions, which is straightforward, apart from the inversion back to the density function. An objection to such methods is that they give no insight into the probabilistic nature of the proble.     

THE THEORY OF COMPETING RISKS1

The theory of competing risks is motivated, certain aspects are reviewed critically, and some extensions are indicated. A unified formulation of the theory is given covering dependent as well as independent risks. The relations between various functions useful in the theory are made explicit. In a historical note a valuable early result is put into modern notation. The currently controversial subject of identifiability when risks are dependent is discussed and it is indicated under what conditions some of the difficulties raised can be overcome. Consequences of assuming proportional hazard rates are set out. New conditions are provided under which this assumption holds and it is shown how the assumption may be tested. Some concluding remarks deal with limitations of the theory and point out areas needing further work.     

CERTAIN BOUNDS CONCERNING CHARACTERISTIC FUNCTIONS1

The largest value of the constant c for which holds over the class of random variables X with non-zero mean and finite second moment, is c=π. Let the random variable (r.v.) X with distribution function F(·) have non-zero mean and finite second moment. In studying a certain random walk problem (Daley, 1976) we sought a bound on the characteristic function of the form for some positive constant c. Of course the inequality is non-trivial only provided that . This note establishes that the best possible constant c =π. The wider relevance of the result is we believe that it underlines the use of trigonometric inequalities in bounding the (modulus of a) c.f. (see e.g. the truncation inequalities in §12.4 of Loève (1963)). In the present case the bound thus obtained is the best possible bound, and is better than the bound (2) |1-?(θ)| ≥ |θEX|-θ²EX²\2 obtained by applying the triangular inequality to the relation which follows from a two-fold integration by parts in the defining equation (*). The treatment of the counter-example furnished below may also be of interest. To prove (1) with c=π, recall that sin u > u(1-u/π) (all real u), so Since |E sinθX|-|E sin(-θX)|, the modulus sign required in (1) can be inserted into (4). Observe that since sin u > u for u < 0, it is possible to strengthen (4) to (denoting max(0,x) by x₊) To show that c=π is the best possible constant in (1), assume without loss of generality that EX > 0, and take θ > 0. Then (1) is equivalent to (6) c < θEX²/{EX-|1-?(θ)|/θ} for all θ > 0 and all r.v.s. X with EX > 0 and EX². Consider the r.v. where 0 < x < 1 and 0 < γ < ∞. Then EX=1, EX²=1+γx², From (4) it follows that |1-?(θ)| > 0 for 0 < |θ| <π|EX|/EX² but in fact this positivity holds for 0 < |θ| < 2π|EX|/EX² because by trigonometry and the Cauchy-Schwartz inequality, |1-?(θ)| > |Re(1-?(θ))| = |E(1-cosθX)| = 2|E sin²θX/2| (10) >2(E sinθX/2)² (11) >(|θEX|-θ²EX²/2π)²/2 > 0, the inequality at (11) holding provided that |θEX|-θ²EX²/2π > 0, i.e., that 0 < |θ| < 2π|EX|/EX². The random variable X at (7) with x= 1 shows that the range of positivity of |1-?(θ)| cannot in general be extended. If X is a non-negative r.v. with finite positive mean, then the identity shows that (1-?(θ))/iθEX is the c.f. of a non-negative random variable, and hence (13) |1-?(θ)| < |θEX| (all θ). This argument fans if pr{X < 0}pr{X> 0} > 0, but as a sharper alternative to (14) |1-?(θ)| < |θE|X||, we note (cf. (2) and (3)) first that (15) |1-?(θ)| < |θEX| +θ²EX²/2. For a bound that is more precise for |θ| close to 0, |1-?(θ)|²= (Re(1-?(θ)))²+ (Im?(θ))² <(θ²EX²/2)²+(|θEX| +θ²EX²_-/π)², so (16) |1-?(θ)| <(|θEX| +θ²EX²_-/π) + |θ|³(EX²)²/8|EX|.     

TECHNIQUES OF COVARIANCE STRUCTURAL ANALYSIS1

The paper delineates the scope and important features of covariance structural analysis in which some pattern can be postulated a priori for the covariance matrix. The coverage given also includes a brief review of the published work in this area with illustration of statistical tests based, on principles of maximum likelihood and union-intersection. Few results on the computational ease under the assumption of particular covariance structures are reported. A few unsolved problems in the area of structural analysis are briefly mentioned.     

IN JUSTIFICATION OF WALD'S CRITERION OF EFFICIENCY OF EXPERIMENTAL DESIGNS IN RELATION TO THE WEIGHING PROBLEM1

Wald (1943), among others, examined the question of efficiency of experimental designs in general and suggested the criterion of maximization of the det. [X'X] for the purpose, where X denotes the design matrix. In the same context, Ehrenfeld (1955) recommended the maximization of the minimum eigenvalue of [X'X] as the desideratum. It has been shown in this note that for weighing designs in general maximization of det. [X'X] may be preferred to the latter criterion.     

A general method of constructing E(s2)-optimal supersaturated designs

There has been much recent interest in supersaturated designs and their application in factor screening experiments. Supersaturated designs have mainly been constructed by using the E ( s ²)-optimality criterion originally proposed by Booth and Cox in 1962. However, until now E ( s ²)-optimal designs have only been established with certainty for n experimental runs when the number of factors m is a multiple of n-1 , and in adjacent cases where m = q ( n -1) + r (| r | 2, q an integer). A method of constructing E ( s ²)-optimal designs is presented which allows a reasonably complete solution to be found for various numbers of runs n including n ,=8 12, 16, 20, 24, 32, 40, 48, 64.     

The L2 Rate of Convergence for Event History Regression with Time-dependent Covariates

Consider repeated events of multiple kinds that occur according to a right-continuous semi-Markov process whose transition rates are influenced by one or more time-dependent covariates. The logarithms of the intensities of the transitions from one state to another are modelled as members of a linear function space, which may be finite- or infinite-dimensional. Maximum likelihood estimates are used, where the maximizations are taken over suitably chosen finite-dimensional approximating spaces. It is shown that the L ₂ rates of convergence of the maximum likelihood estimates are determined by the approximation power and dimension of the approximating spaces. The theory is applied to a functional ANOVA model, where the logarithms of the intensities are approximated by functions having the form of a specified sum of a constant term, main effects (functions of one variable), and interaction terms (functions of two or more variables). It is shown that the curse of dimensionality can be ameliorated if only main effects and low-order interactions are considered in functional ANOVA models.     

DISTRIBUTIONS IN R WITH ROTATIONAL SYMMETRIES1

Let X ∈ R be a random vector with a distribution which is invariant under rotations within the subspaces V_j (dim V_j. = q_j) whose direct sum is R. The large sample distributions of the eigenvalues and vectors of M_n= n^-1Σⁿ_l x_ix_i are studied. In particular it is shown that several eigenvalue results of Anderson & Stephens (1972) for uniformly distributed unit vectors hold more generally.     

ON THE DISTRIBUTIONS OF ORDER STATISTICS1

Approximations to the distributions of order statistics based on x²t are obtained. These are easy to compute and provide reasonably accurate values for the percentage points and probability integrals of the distributions.