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Power Properties (power + property)

Distribution by Scientific Domains

Mathematics and Statistics	50%
Business, Economics, Finance and Accounting	50%

Selected Abstracts

A Conditional Likelihood Ratio Test for Structural Models

ECONOMETRICA, Issue 4 2003
Marcelo J. Moreira
This paper develops a general method for constructing exactly similar tests based on the conditional distribution of nonpivotal statistics in a simultaneous equations model with normal errors and known reduced-form covariance matrix. These tests are shown to be similar under weak-instrument asymptotics when the reduced-form covariance matrix is estimated and the errors are non-normal. The conditional test based on the likelihood ratio statistic is particularly simple and has good power properties. Like the score test, it is optimal under the usual local-to-null asymptotics, but it has better power when identification is weak. [source]

Evaluating Specification Tests for Markov-Switching Time-Series Models

JOURNAL OF TIME SERIES ANALYSIS, Issue 4 2008
Daniel R. Smith
C12; C15; C22 Abstract., We evaluate the performance of several specification tests for Markov regime-switching time-series models. We consider the Lagrange multiplier (LM) and dynamic specification tests of Hamilton (1996) and Ljung,Box tests based on both the generalized residual and a standard-normal residual constructed using the Rosenblatt transformation. The size and power of the tests are studied using Monte Carlo experiments. We find that the LM tests have the best size and power properties. The Ljung,Box tests exhibit slight size distortions, though tests based on the Rosenblatt transformation perform better than the generalized residual-based tests. The tests exhibit impressive power to detect both autocorrelation and autoregressive conditional heteroscedasticity (ARCH). The tests are illustrated with a Markov-switching generalized ARCH (GARCH) model fitted to the US dollar,British pound exchange rate, with the finding that both autocorrelation and GARCH effects are needed to adequately fit the data. [source]

An Omnibus Test for Univariate and Multivariate Normality,

OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 2008
Jurgen A. Doornik
Abstract We suggest a convenient version of the omnibus test for normality, using skewness and kurtosis based on Shenton and Bowman [Journal of the American Statistical Association (1977) Vol. 72, pp. 206,211], which controls well for size, for samples as low as 10 observations. A multivariate version is introduced. Size and power are investigated in comparison with four other tests for multivariate normality. The first power experiments consider the whole skewness,kurtosis plane; the second use a bivariate distribution which has normal marginals. It is concluded that the proposed test has the best size and power properties of the tests considered. [source]

FINITE SAMPLE EFFECTS OF PURE SEASONAL MEAN SHIFTS ON DICKEY,FULLER TESTS: A SIMULATION STUDY

THE MANCHESTER SCHOOL, Issue 5 2008
ARTUR C. B. DA SILVA LOPESArticle first published online: 18 AUG 200
In this paper, it is demonstrated by simulation that, contrary to a widely held belief, pure seasonal mean shifts,i.e. seasonal structural breaks which affect only the seasonal cycle,really do matter for Dickey,Fuller long-run unit root tests. Both size and power properties are affected by such breaks but using the t -sig method for lag selection induces a stabilizing effect. Although most results are reassuring when the t -sig method is used, some concern with this type of breaks cannot be disregarded. Further evidence on the poor performance of the t -sig method for quarterly time series in standard (no-break) cases is also presented. [source]

Resampling-Based Empirical Bayes Multiple Testing Procedures for Controlling Generalized Tail Probability and Expected Value Error Rates: Focus on the False Discovery Rate and Simulation Study

BIOMETRICAL JOURNAL, Issue 5 2008
Sandrine Dudoit
Abstract This article proposes resampling-based empirical Bayes multiple testing procedures for controlling a broad class of Type I error rates, defined as generalized tail probability (gTP) error rates, gTP (q,g) = Pr(g (Vn,Sn) > q), and generalized expected value (gEV) error rates, gEV (g) = E [g (Vn,Sn)], for arbitrary functions g (Vn,Sn) of the numbers of false positives Vn and true positives Sn. Of particular interest are error rates based on the proportion g (Vn,Sn) = Vn /(Vn + Sn) of Type I errors among the rejected hypotheses, such as the false discovery rate (FDR), FDR = E [Vn /(Vn + Sn)]. The proposed procedures offer several advantages over existing methods. They provide Type I error control for general data generating distributions, with arbitrary dependence structures among variables. Gains in power are achieved by deriving rejection regions based on guessed sets of true null hypotheses and null test statistics randomly sampled from joint distributions that account for the dependence structure of the data. The Type I error and power properties of an FDR-controlling version of the resampling-based empirical Bayes approach are investigated and compared to those of widely-used FDR-controlling linear step-up procedures in a simulation study. The Type I error and power trade-off achieved by the empirical Bayes procedures under a variety of testing scenarios allows this approach to be competitive with or outperform the Storey and Tibshirani (2003) linear step-up procedure, as an alternative to the classical Benjamini and Hochberg (1995) procedure. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

Analyzing Incomplete Data Subject to a Threshold using Empirical Likelihood Methods: An Application to a Pneumonia Risk Study in an ICU Setting

BIOMETRICS, Issue 1 2010
Jihnhee Yu
Summary The initial detection of ventilator-associated pneumonia (VAP) for inpatients at an intensive care unit needs composite symptom evaluation using clinical criteria such as the clinical pulmonary infection score (CPIS). When CPIS is above a threshold value, bronchoalveolar lavage (BAL) is performed to confirm the diagnosis by counting actual bacterial pathogens. Thus, CPIS and BAL results are closely related and both are important indicators of pneumonia whereas BAL data are incomplete. To compare the pneumonia risks among treatment groups for such incomplete data, we derive a method that combines nonparametric empirical likelihood ratio techniques with classical testing for parametric models. This technique augments the study power by enabling us to use any observed data. The asymptotic property of the proposed method is investigated theoretically. Monte Carlo simulations confirm both the asymptotic results and good power properties of the proposed method. The method is applied to the actual data obtained in clinical practice settings and compares VAP risks among treatment groups. [source]