Mervyn J. Silvapulle

Epileptology of the first-seizure presentation: a clinical, electroencephalographic, and magnetic resonance imaging study of 300 consecutive patients

BACKGROUNDnPrognosis and treatment of the first seizure depends on identification of a specific epilepsy syndrome, yet patients with first seizures are generally regarded as a homogeneous group. We studied whether it is possible to diagnose specific epilepsy syndromes promptly by use of standard clinical methods, electroencephalography (EEG) and magnetic resonance imaging (MRI).nnnMETHODSn300 consecutive adults and children presented with unexplained seizures. We systematically collected clinical data from patients and witnesses, and attempted to obtain an EEG within 24 h of the seizure. Where the EEG was negative, a sleep-deprived EEG was done. MRI was done electively.nnnFINDINGSnA generalised or partial epilepsy syndrome was clinically diagnosed in 141 (47%) patients. Subsequent analysis showed that only three of these clinical diagnoses were incorrect. Addition of the EEG data enabled us to diagnose an epilepsy syndrome in 232 (77%) patients. EEG within 24 h was more useful in diagnosis of epileptiform abnormalities than later EEG (51 vs 34%). Neuroimaging showed 38 epileptogenic lesions, including 17 tumours. There were no lesions in patients for whom generalised epilepsy was confirmed by EEG. Our final diagnoses were: generalised epilepsy (23% of patients); partial epilepsy (58%); and unclassified (19%).nnnINTERPRETATIONnAn epilepsy syndrome can be diagnosed in most first-seizure patients. Ideally, an EEG should be obtained within 24 h of the seizure followed by a sleep deprived EEG if necessary. MRI aids diagnosis and should be done for all patients except for those with idiopathic generalised epilepsies and for children with benign rolandic epilepsy.

Comparison of semiparametric and parametric methods for estimating copulas

Copulas have attracted significant attention in the recent literature for modeling multivariate observations. An important feature of copulas is that they enable us to specify the univariate marginal distributions and their joint behavior separately. The copula parameter captures the intrinsic dependence between the marginal variables and it can be estimated by parametric or semiparametric methods. For practical applications, the so called inference function for margins (IFM) method has emerged as the preferred fully parametric method because it is close to maximum likelihood (ML) in approach and is easier to implement. The purpose of this paper is to compare the ML and IFM methods with a semiparametric (SP) method that treats the univariate marginal distributions as unknown functions. In this paper, we consider the SP method proposed by Genest et al. [1995. A semiparametric estimation procedure of dependence parameters in multivariate families of distributions. Biometrika 82(3), 543-552], which has attracted considerable interest in the literature. The results of an extensive simulation study reported here show that the ML/IFM methods are nonrobust against misspecification of the marginal distributions, and that the SP method performs better than the ML and IFM methods, overall. A data example on household expenditure is used to illustrate the application of various data analytic methods for applying the SP method, and to compare and contrast the ML, IFM and SP methods. The main conclusion is that, in terms of statistical computations and data analysis, the SP method is better than ML and IFM methods when the marginal distributions are unknown which is almost always the case in practice.

Constrained statistical inference : inequality, order, and shape restrictions.

Dedication. Preface. 1. Introduction. 1.1 Preamble. 1.2 Examples. 1.3 Coverage and Organization of the Book. 2. Comparison of Population Means and Isotonic Regression. 2.1 Ordered Hypothesis Involving Population Means. 2.2 Test of Inequality Constraints. 2.3 Isotonic Regression. 2.4 Isotonic Regression: Results Related to Computational Formulas. 3. Two Inequality Constrained Tests on Normal Means. 3.1 Introduction. 3.2 Statement of Two General Testing Problems. 3.3 Theory: The Basics in 2 Dimensions. 3.4 Chi-bar-square Distribution. 3.5 Computing the Tail Probabilities of chi-bar-square Distributions. 3.6 Detailed Results relating to chi-bar-square Distributions. 3.7 LRT for Type A Problems: V is known. 3.8 LRT for Type B Problems: V is known. 3.9 Inequality Constrained Tests in the Linear Model. 3.10 Tests When V is known. 3.11 Optimality Properties. 3.12 Appendix 1: Convex Cones. 3.13 Appendix B. Proofs. 4. Tests in General Parametric Models. 4.1 Introduction. 2.2 Preliminaries. 4.3 Tests of Rtheta = 0 against Rtheta 0. 4.4 Tests of h(theta) = 0. 4.5 An Overview of Score Tests with no Inequality Constraints. 4.6 Local Score-type Tests of Ho : psi = 0 vs H1 : psi &epsis PSI. 4.7 Approximating Cones and Tangent Cones. 4.8 General Testing Problems. 4.9 Properties of the mle When the True Value is on the Boundary. 5. Likelihood and Alternatives. 5.1 Introduction. 5.2 The Union-Intersection principle. 5.3 Intersection Union Tests (IUT). 5.4 Nanparametrics. 5.5 Restricted Alternatives and Simes-type Procedures. 5.6 Concluding Remarks. 6. Analysis of Categorical Data. 6.1 Motivating Examples. 6.2 Independent Binomial Samples. 6.3 Odds Ratios and Monotone Dependence. 6.4 Analysis of 2 x c Contingency Tables. 6.5 Test to Establish that Treatment is Better than Control. 6.6 Analysis of r x c Tables. 6.7 Square Tables and Marginal Homogeneity. 6.8 Exact Conditional Tests. 6.9 Discussion. 7. Beyond Parametrics. 7.1 Introduction. 7.2 Inference on Monotone Density Function. 7.3 Inference on Unimodal Density Function. 7.4 Inference on Shape Constrained Hazard Functionals. 7.5 Inference on DMRL Functions. 7.6 Isotonic Nonparametric Regression: Estimation. 7.7 Shape Constraints: Hypothesis Testing. 8. Bayesian Perspectives. 8.1 Introduction. 8.2 Statistical Decision Theory Motivations. 8.3 Steins Paradox and Shrinkage Estimation. 8.4 Constrained Shrinkage Estimation. 8.5 PC and Shrinkage Estimation in CSI. 8.6 Bayes Tests in CSI. 8.7 Some Decision Theoretic Aspects: Hypothesis Testing. 9. Miscellaneous Topics. 9.1 Two-sample Problem with Multivariate Responses. 9.2 Testing that an Identified Treatment is the Best: The mini-test. 9.3 Cross-over Interaction. 9.4 Directed Tests. Bibliography. Index.

A Score Test against One-Sided Alternatives

Abstract A score-type statistic, T s , is introduced for testing H: ψ = 0 against K: ψ ≥ 0 and more general one-sided hypotheses when nuisance parameters may be present; ψ is a vector parameter. The main advantages of T s , are that it requires estimation of the model only under the null hypothesis and that, it is asymptotically equivalent to the likelihood ratio statistic; these are precisely the reasons for the popularity of the score tests for testing against two-sided alternatives. In this sense, T s preserves the main attractive features of the classical two-sided score test. The theoretical results are presented in a general framework where the likelihood-based score function is replaced by an estimating function so that the test is applicable even if the exact population distribution is unknown. Computation of T s , is simplified by the fact that it can be computed easily once the corresponding two-sided statistic has been computed. The relevance and simplicity of T s are illustrated by discussing ...

On tests against one-sided hypotheses in some generalized linear models.

One-sided hypotheses arise naturally in many situations. When testing against such hypotheses, it is desirable to take the available one-sided information into account, rather than simply applying a two-sided test. What we expect to gain by applying a one-sided test instead of a two-sided test is an increase in the power of the test. We consider various tests of one-sided hypotheses in a class of models that includes generalized linear and Cox regression models. The tests are likelihood ratio, Wald, score, generalized distance, and a Pearson chi-square. It is shown that these test statistics are asymptomatically equivalent in terms of local power; this is a generalization of the well-known corresponding result for two-sided alternatives. Two examples are also discussed. They are on (1) testing for interaction in binomial response models, and (2) comparison of treatments with ordinal categorical responses.

A Test in the Presence of Nuisance Parameters

Abstract We are interested in testing Ψ = 0 against an alternative in the presence of some nuisance parameter λ. The usual procedure for such problems is to use a test statistic that is a function of the data only. Let q(λ) denote the p-value at a given value λ. If q(λ) does not depend on λ, then in principle we can apply this procedure. However, a major difficulty that arises in many situations is that q(λ) depends on λ and therefore cannot be used as a p-value. In such cases, the usual approach is to define the p-value as the supremum of q(λ) over the nuisance parameter space. Because this approach ignores sample information about λ, it may be unnecessarily conservative; this is a serious problem in order restricted inference. To overcome this, I propose the following. Obtain, say, a 99% confidence region for λ under the null hypothesis. Now, for a given λ, let T(λ) be a test statistic and r(λ) be the p-value. The test procedure is to reject the null hypothesis if {0.01 + supremum of r(λ) over the 99% c...

Testing for Temporal Asymmetry in the Price-Volume Relationship

This paper presents some evidence for the presence of temporal asymmetry in the price-volume relationship in the crude oil futures market. By using threshold models we show that there is bidirectional causality between volume and prices, whereas the conventional model that assumes symmetry can only detect unidirectional causality. The results also show that the price-volume relationship is asymmetric, in the sense that negative price and volume changes have stronger effects (on each other) than positive changes. Some explanations for asymmetry in the price-volume relationship are suggested.

Robust Wald-Type Tests of One-Sided Hypotheses in the Linear Model

Abstract Let us consider the linear model Y = Xθ + E in the usual matrix notation, where the errors are iid. Our main objective is to develop robust Wald-type tests for a large class of hypotheses on θ; by robust we mean robustness in terms of size and power against long-tailed error distributions. First assume that the error distribution is symmetric about the origin. Let L and be the least squares and a robust estimator of θ. Assume that they are asymptotically normal about θ with covariance matrices σ 2(XtX)–1 and τ 2(XtX)–1, respectively. So could be an M estimator or a high breakdown point estimator. Robust Wald-type tests based on (denoted by RW) are studied here for testing a large class of one-sided hypotheses on θ. It is shown that the asymptotic null distribution of RW and that of the usual Wald-type statistic based on L (denoted by W) are the same. This is a useful result since the critical values and procedures for computing the p values for W are directly applicable to RW as well. A more impo...

Experience in adverse events detection in an emergency department: Nature of events

Objective:u2002 The study was performed to determine the nature of adverse events in an ED.

A Curious Example Involving the Likelihood Ratio Test against One-Sided Hypotheses

Abstract An example is presented in which the following curious phenomenon is observed. Let X ∼ N(μ, Ω), where X = (X 1, X 2), μ = (μ1, μ2), Ω11 = Ω22 = 1, and Ω12 = Ω21 = .90. For a random sample from N(μ, Ω) suppose that the sample mean = (−3, −2); thus every observed value of X 1 and X 2 can be negative. Then, for a suitable hypothesis testing problem with μ1 = 0 being the null hypothesis and being the test statistic, one would accept that μ1 < 0; and similarly, one would accept that μ2 < 0. However, the likelihood ratio test of H 0: μ = 0 against H 1: μ ≥ 0 and μ ≠ 0, would reject H 0 and accept H 1. We do recognize that the hypothesis H 1: μ ≥ 0 and μ ≠ 0 does not allow negative values for μ1 or μ2. Nevertheless, the phenomenon is curious.