Comparative resilience of clinical perimetric tests to induced levels of intraocular straylight.

PURPOSE: To investigate the effect of intraocular straylight (IOS) induced by white opacity filters (WOF) on threshold measurements for stimuli employed in three perimeters: standard automated perimetry (SAP), pulsar perimetry (PP) and the Moorfields motion displacement test (MDT).¦METHODS: Four healthy young (24-28 years old) observers were tested six times with each perimeter, each time with one of five different WOFs and once without, inducing various levels of IOS (from 10% to 200%). An increase in IOS was measured with a straylight meter. The change in sensitivity from baseline was normalized, allowing comparison of standardized (z) scores (change divided by the SD of normative values) for each instrument.¦RESULTS: SAP and PP thresholds were significantly affected (P < 0.001) by moderate to large increases in IOS (50%-200%). The drop in motion displacement (MD) from baseline with WOF 5, was approximately 5 dB, in both SAP and PP which represents a clinically significant loss; in contrast the change in MD with MDT was on average 1 minute of arc, which is not likely to indicate a clinically significant loss.¦CONCLUSIONS: The Moorfields MDT is more robust to the effects of additional straylight in comparison with SAP or PP.

Performance aux tests d'intelligence : vers une inversion de l'effet Flynn ?

Des nombreuses études ont montré une augmentation des scores aux tests d'aptitudes à travers les générations (« effet Flynn »). Différentes hypothèses d'ordre biologique, social et/ou éducationnels ont été élaborées afin d'expliquer ce phénomène. L'objectif de cette recherche est d'examiner l'évolution des performances aux tests d'aptitudes sur la base d'étalonnages datant de 1991 et de 2002. Les résultats suggèrent une inversion non homogène de l'effet Flynn. La diminution concerne plus particulièrement les tests d'aptitudes scolaires, comme ceux évaluant le facteur verbal et numérique. Cette étude pourrait refléter un changement de l'importance accordée aux différentes aptitudes peu évaluées en orientation scolaire et professionnelle.

Reduction in the use of diagnostic tests in infants with risk factors for early-onset neonatal sepsis does not delay antibiotic treatment.

Despite a low positive predictive value, diagnostic tests such as complete blood count (CBC) and C-reactive protein (CRP) are commonly used to evaluate whether infants with risk factors for early-onset neonatal sepsis (EOS) should be treated with antibiotics. We investigated the impact of implementing a protocol aiming at reducing the number of diagnostic tests in infants with risk factors for EOS in order to compare the diagnostic performance of repeated clinical examination with CBC and CRP measurement. The primary outcome was the time between birth and the first dose of antibiotics in infants treated for suspected EOS. Among the 11,503 infants born at ≥35 weeks during the study period, 222 were treated with antibiotics for suspected EOS. The proportion of infants receiving antibiotics for suspected EOS was 2.1% and 1.7% before and after the change of protocol (p = 0.09). Reduction of diagnostic tests was associated with earlier antibiotic treatment in infants treated for suspected EOS (hazard ratio 1.58; 95% confidence interval [CI] 1.20-2.07; p <0.001), and in infants with neonatal infection (hazard ratio 2.20; 95% CI 1.19-4.06; p = 0.01). There was no difference in the duration of hospital stay nor in the proportion of infants requiring respiratory or cardiovascular support before and after the change of protocol. Reduction of diagnostic tests such as CBC and CRP does not delay initiation of antibiotic treatment in infants with suspected EOS. The importance of clinical examination in infants with risk factors for EOS should be emphasised.

Patients' understanding of blood tests and attitudes to HIV screening in the emergency department of a Swiss teaching hospital: a cross-sectional observational study.

BACKGROUND: In Switzerland, patients may undergo "blood tests" without being informed what these are screening for. Inadequate doctor-patient communication may result in patient misunderstanding. We examined what patients in the emergency department (ED) believed they had been screened for and explored their attitudes to routine (non-targeted) human immunodeficiency virus (HIV) screening. METHODS: Between 1st October 2012 and 28th February 2013, a questionnaire-based survey was conducted among patients aged 16-70 years old presenting to the ED of Lausanne University Hospital. Patients were asked: (1) if they believed they had been screened for HIV; (2) if they agreed in principle to routine HIV screening and (3) if they agreed to be HIV tested during their current ED visit. RESULTS: Of 466 eligible patients, 411 (88%) agreed to participate. Mean age was 46 ± 16 years; 192 patients (47%) were women; 366 (89%) were Swiss or European; 113 (27%) believed they had been screened for HIV, the proportion increasing with age (p ≤0.01), 297 (72%) agreed in principle with routine HIV testing in the ED, and 138 patients (34%) agreed to be HIV tested during their current ED visit. CONCLUSION: In this ED population, 27% believed incorrectly they had been screened for HIV. Over 70% agreed in principle with routine HIV testing and 34% agreed to be tested during their current visit. These results demonstrate willingness among patients concerning routine HIV testing in the ED and highlight a need for improved doctor-patient communication about what a blood test specifically screens for.

The Reluctance of Cattle to Change a Learned Choice May Confound Preference Tests

The conclusion of the article states "it appears that previously learned choices may affect future choices in Y-mazes for cattle. Another area that needs to be researched is the effects of a mildly aversive treatment versus a severely aversive treatment on the tendency of a bovine to resist changing a learned choice".

Simulation-Based Finite and Large Sample Tests in Multivariate Regressions.

In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g., the parametric bootstrap) which may be applied when the bounds are not conclusive.

Exact Tests for Contemporaneous Correlation of Disturbances in Seemingly Unrelated Regressions.

This paper proposes finite-sample procedures for testing the SURE specification in multi-equation regression models, i.e. whether the disturbances in different equations are contemporaneously uncorrelated or not. We apply the technique of Monte Carlo (MC) tests [Dwass (1957), Barnard (1963)] to obtain exact tests based on standard LR and LM zero correlation tests. We also suggest a MC quasi-LR (QLR) test based on feasible generalized least squares (FGLS). We show that the latter statistics are pivotal under the null, which provides the justification for applying MC tests. Furthermore, we extend the exact independence test proposed by Harvey and Phillips (1982) to the multi-equation framework. Specifically, we introduce several induced tests based on a set of simultaneous Harvey/Phillips-type tests and suggest a simulation-based solution to the associated combination problem. The properties of the proposed tests are studied in a Monte Carlo experiment which shows that standard asymptotic tests exhibit important size distortions, while MC tests achieve complete size control and display good power. Moreover, MC-QLR tests performed best in terms of power, a result of interest from the point of view of simulation-based tests. The power of the MC induced tests improves appreciably in comparison to standard Bonferroni tests and, in certain cases, outperforms the likelihood-based MC tests. The tests are applied to data used by Fischer (1993) to analyze the macroeconomic determinants of growth.

Économétrie, théorie des tests et philosophie des sciences

Dans ce texte, nous revoyons certains développements récents de l’économétrie qui peuvent être intéressants pour des chercheurs dans des domaines autres que l’économie et nous soulignons l’éclairage particulier que l’économétrie peut jeter sur certains thèmes généraux de méthodologie et de philosophie des sciences, tels la falsifiabilité comme critère du caractère scientifique d’une théorie (Popper), la sous-détermination des théories par les données (Quine) et l’instrumentalisme. En particulier, nous soulignons le contraste entre deux styles de modélisation - l’approche parcimonieuse et l’approche statistico-descriptive - et nous discutons les liens entre la théorie des tests statistiques et la philosophie des sciences.

Simulation-Based Finite-Sample Tests for Heteroskedasticity and ARCH Effects.

A wide range of tests for heteroskedasticity have been proposed in the econometric and statistics literature. Although a few exact homoskedasticity tests are available, the commonly employed procedures are quite generally based on asymptotic approximations which may not provide good size control in finite samples. There has been a number of recent studies that seek to improve the reliability of common heteroskedasticity tests using Edgeworth, Bartlett, jackknife and bootstrap methods. Yet the latter remain approximate. In this paper, we describe a solution to the problem of controlling the size of homoskedasticity tests in linear regression contexts. We study procedures based on the standard test statistics [e.g., the Goldfeld-Quandt, Glejser, Bartlett, Cochran, Hartley, Breusch-Pagan-Godfrey, White and Szroeter criteria] as well as tests for autoregressive conditional heteroskedasticity (ARCH-type models). We also suggest several extensions of the existing procedures (sup-type of combined test statistics) to allow for unknown breakpoints in the error variance. We exploit the technique of Monte Carlo tests to obtain provably exact p-values, for both the standard and the new tests suggested. We show that the MC test procedure conveniently solves the intractable null distribution problem, in particular those raised by the sup-type and combined test statistics as well as (when relevant) unidentified nuisance parameter problems under the null hypothesis. The method proposed works in exactly the same way with both Gaussian and non-Gaussian disturbance distributions [such as heavy-tailed or stable distributions]. The performance of the procedures is examined by simulation. The Monte Carlo experiments conducted focus on : (1) ARCH, GARCH, and ARCH-in-mean alternatives; (2) the case where the variance increases monotonically with : (i) one exogenous variable, and (ii) the mean of the dependent variable; (3) grouped heteroskedasticity; (4) breaks in variance at unknown points. We find that the proposed tests achieve perfect size control and have good power.

Logique et tests d'hypotheses: reflexions sur les problemes mal poses en econometrie

Dans ce texte, nous analysons les développements récents de l’économétrie à la lumière de la théorie des tests statistiques. Nous revoyons d’abord quelques principes fondamentaux de philosophie des sciences et de théorie statistique, en mettant l’accent sur la parcimonie et la falsifiabilité comme critères d’évaluation des modèles, sur le rôle de la théorie des tests comme formalisation du principe de falsification de modèles probabilistes, ainsi que sur la justification logique des notions de base de la théorie des tests (tel le niveau d’un test). Nous montrons ensuite que certaines des méthodes statistiques et économétriques les plus utilisées sont fondamentalement inappropriées pour les problèmes et modèles considérés, tandis que de nombreuses hypothèses, pour lesquelles des procédures de test sont communément proposées, ne sont en fait pas du tout testables. De telles situations conduisent à des problèmes statistiques mal posés. Nous analysons quelques cas particuliers de tels problèmes : (1) la construction d’intervalles de confiance dans le cadre de modèles structurels qui posent des problèmes d’identification; (2) la construction de tests pour des hypothèses non paramétriques, incluant la construction de procédures robustes à l’hétéroscédasticité, à la non-normalité ou à la spécification dynamique. Nous indiquons que ces difficultés proviennent souvent de l’ambition d’affaiblir les conditions de régularité nécessaires à toute analyse statistique ainsi que d’une utilisation inappropriée de résultats de théorie distributionnelle asymptotique. Enfin, nous soulignons l’importance de formuler des hypothèses et modèles testables, et de proposer des techniques économétriques dont les propriétés sont démontrables dans les échantillons finis.

Exact Nonparametric Two-Sample Homogeneity Tests for Possibly Discrete Distributions.

In this paper, we study several tests for the equality of two unknown distributions. Two are based on empirical distribution functions, three others on nonparametric probability density estimates, and the last ones on differences between sample moments. We suggest controlling the size of such tests (under nonparametric assumptions) by using permutational versions of the tests jointly with the method of Monte Carlo tests properly adjusted to deal with discrete distributions. We also propose a combined test procedure, whose level is again perfectly controlled through the Monte Carlo test technique and has better power properties than the individual tests that are combined. Finally, in a simulation experiment, we show that the technique suggested provides perfect control of test size and that the new tests proposed can yield sizeable power improvements.

Some Robust Exact Results on Sample Autocorrelations and Tests of Randomness

Ce Texte Presente Plusieurs Resultats Exacts Sur les Seconds Moments des Autocorrelations Echantillonnales, Pour des Series Gaussiennes Ou Non-Gaussiennes. Nous Donnons D'abord des Formules Generales Pour la Moyenne, la Variance et les Covariances des Autocorrelations Echantillonnales, Dans le Cas Ou les Variables de la Serie Sont Interchangeables. Nous Deduisons de Celles-Ci des Bornes Pour les Variances et les Covariances des Autocorrelations Echantillonnales. Ces Bornes Sont Utilisees Pour Obtenir des Limites Exactes Sur les Points Critiques Lorsqu'on Teste le Caractere Aleatoire D'une Serie Chronologique, Sans Qu'aucune Hypothese Soit Necessaire Sur la Forme de la Distribution Sous-Jacente. Nous Donnons des Formules Exactes et Explicites Pour les Variances et Covariances des Autocorrelations Dans le Cas Ou la Serie Est un Bruit Blanc Gaussien. Nous Montrons Que Ces Resultats Sont Aussi Valides Lorsque la Distribution de la Serie Est Spheriquement Symetrique. Nous Presentons les Resultats D'une Simulation Qui Indiquent Clairement Qu'on Approxime Beaucoup Mieux la Distribution des Autocorrelations Echantillonnales En Normalisant Celles-Ci Avec la Moyenne et la Variance Exactes et En Utilisant la Loi N(0,1) Asymptotique, Plutot Qu'en Employant les Seconds Moments Approximatifs Couramment En Usage. Nous Etudions Aussi les Variances et Covariances Exactes D'autocorrelations Basees Sur les Rangs des Observations.

Tests of Joint Hypotheses for Time Series Regression with a Unit Root

This Paper Studies Tests of Joint Hypotheses in Time Series Regression with a Unit Root in Which Weakly Dependent and Heterogeneously Distributed Innovations Are Allowed. We Consider Two Types of Regression: One with a Constant and Lagged Dependent Variable, and the Other with a Trend Added. the Statistics Studied Are the Regression \"F-Test\" Originally Analysed by Dickey and Fuller (1981) in a Less General Framework. the Limiting Distributions Are Found Using Functinal Central Limit Theory. New Test Statistics Are Proposed Which Require Only Already Tabulated Critical Values But Which Are Valid in a Quite General Framework (Including Finite Order Arma Models Generated by Gaussian Errors). This Study Extends the Results on Single Coefficients Derived in Phillips (1986A) and Phillips and Perron (1986).

Some Additional Specification Tests for Generalized Method of Moments Estimators with Macro-Economic Applications Part I : Theory

In This Paper Several Additional Gmm Specification Tests Are Studied. a First Test Is a Chow-Type Test for Structural Parameter Stability of Gmm Estimates. the Test Is Inspired by the Fact That \"Taste and Technology\" Parameters Are Uncovered. the Second Set of Specification Tests Are Var Encompassing Tests. It Is Assumed That the Dgp Has a Finite Var Representation. the Moment Restrictions Which Are Suggested by Economic Theory and Exploited in the Gmm Procedure Represent One Possible Characterization of the Dgp. the Var Is a Different But Compatible Characterization of the Same Dgp. the Idea of the Var Encompassing Tests Is to Compare Parameter Estimates of the Euler Conditions and Var Representations of the Dgp Obtained Separately with Parameter Estimates of the Euler Conditions and Var Representations Obtained Jointly. There Are Several Ways to Construct Joint Systems Which Are Discussed in the Paper. Several Applications Are Also Discussed.

Tests of Conditional Asset Pricing Models in the Brazilian Stock Market

In this paper, we test a version of the conditional CAPM with respect to a local market portfolio, proxied by the Brazilian stock index during the 1976-1992 period. We also test a conditional APT model by using the difference between the 30-day rate (Cdb) and the overnight rate as a second factor in addition to the market portfolio in order to capture the large inflation risk present during this period. The conditional CAPM and APT models are estimated by the Generalized Method of Moments (GMM) and tested on a set of size portfolios created from a total of 25 securities exchanged on the Brazilian markets. The inclusion of this second factor proves to be crucial for the appropriate pricing of the portfolios.