Search results for "testing"
showing 10 items of 1769 documents
Olley–Pakes productivity decomposition: computation and inference
2016
Summary We show how a moment-based estimation procedure can be used to compute point estimates and standard errors for the two components of the widely used Olley–Pakes decomposition of aggregate (weighted average) productivity. When applied to business level microdata, the procedure allows for autocovariance and heteroscedasticity robust inference and hypothesis testing about, for example, the coevolution of the productivity components in different groups of firms. We provide an application to Finnish firm level data and find that formal statistical inference casts doubt on the conclusions that one might draw on the basis of a visual inspection of the components of the decomposition.
Improvements and Modifications of Tarone's Multiple Test Procedure for Discrete Data
1998
Tarone (1990, Biometrics 46, 515-522) proposed a multiple test procedure for discrete test statistics improving the usual Bonferroni procedure. However, Tarone's procedure is not monotone depending on the predetermined multiple level a. Roth (1998, Journal of Statistical Planning and Inference, in press) developed a monotone version of Tarone's procedure. We present a similar procedure that is both monotone and an improvement of Tarone's proposal. Based on this extension, we derive a step-down procedure that is a corresponding improvement of Holm's (1979, Scandinavian Journal of Statistics 6, 65-70) sequentially rejective procedure. It is shown how adjusted p-values can be computed for the …
Global and multiple test procedures using ordered p-values—a review
2004
This paper reviews global and multiple tests for the combination ofn hypotheses using the orderedp-values of then individual tests. In 1987, Rohmel and Streitberg presented a general method to construct global level α tests based on orderedp-values when there exists no prior knowledge regarding the joint distribution of the corresponding test statistics. In the case of independent test statistics, construction of global tests is available by means of recursive formulae presented by Bicher (1989), Kornatz (1994) and Finner and Roters (1994). Multiple test procedures can be developed by applying the closed test principle using these global tests as building blocks. Liu (1996) proposed represe…
Extending conventional priors for testing general hypotheses in linear models
2007
We consider that observations come from a general normal linear model and that it is desirable to test a simplifying null hypothesis about the parameters. We approach this problem from an objective Bayesian, model-selection perspective. Crucial ingredients for this approach are 'proper objective priors' to be used for deriving the Bayes factors. Jeffreys-Zellner-Siow priors have good properties for testing null hypotheses defined by specific values of the parameters in full-rank linear models. We extend these priors to deal with general hypotheses in general linear models, not necessarily of full rank. The resulting priors, which we call 'conventional priors', are expressed as a generalizat…
Adaptive linear rank tests for eQTL studies
2012
Expression quantitative trait loci (eQTL) studies are performed to identify single-nucleotide polymorphisms that modify average expression values of genes, proteins, or metabolites, depending on the genotype. As expression values are often not normally distributed, statistical methods for eQTL studies should be valid and powerful in these situations. Adaptive tests are promising alternatives to standard approaches, such as the analysis of variance or the Kruskal-Wallis test. In a two-stage procedure, skewness and tail length of the distributions are estimated and used to select one of several linear rank tests. In this study, we compare two adaptive tests that were proposed in the literatur…
Generalization of Jeffreys Divergence-Based Priors for Bayesian Hypothesis Testing
2008
Summary We introduce objective proper prior distributions for hypothesis testing and model selection based on measures of divergence between the competing models; we call them divergence-based (DB) priors. DB priors have simple forms and desirable properties like information (finite sample) consistency and are often similar to other existing proposals like intrinsic priors. Moreover, in normal linear model scenarios, they reproduce the Jeffreys–Zellner–Siow priors exactly. Most importantly, in challenging scenarios such as irregular models and mixture models, DB priors are well defined and very reasonable, whereas alternative proposals are not. We derive approximations to the DB priors as w…
Assessing covariate imbalance in meta-analysis studies.
2010
The main goal of meta-analysis is to combine data across studies or data sets to obtain summary estimates. In this paper, the novelty is to propose a statistical tool to assess a possible covariate imbalance in baseline variables to investigate similarity of trials. We conducted the detection of the covariate imbalance, first, through some graphical comparison of the empirical cumulative distribution functions or ECDFs, which are built by putting together arms or trials according to some risk factor, and second, through some non-parametric tests such as the Kolmogorov–Smirnov and the Anderson–Darling tests. To overcome the huge presence of ties, we conducted the statistical tests on perturbe…
Equivalence Testing With Particle Size Distribution Data: Methods and Applications in the Development of Inhalative Drugs
2017
ABSTRACTKey criteria of the quality of inhalative drugs are assessed in experiments generating so-called particle size distributions as data. Many experiments of that kind are carried out to demonstrate that necessary modifications to whatever part of the manufacturing process do not substantially change basic characteristics of an inhalable drug product. The equivalence testing procedures we derive for that purpose rely on different models accommodating the specific structure of such data and on different ways of specifying the region of nonrelevant differences. For each hypotheses formulation, three different tests are derived (two parametric and one asymptotically distribution-free proce…
Multiple Comparisons of Treatments with Stable Multivariate Tests in a Two‐Stage Adaptive Design, Including a Test for Non‐Inferiority
2000
The application of stabilized multivariate tests is demonstrated in the analysis of a two-stage adaptive clinical trial with three treatment arms. Due to the clinical problem, the multiple comparisons include tests of superiority as well as a test for non-inferiority, where non-inferiority is (because of missing absolute tolerance limits) expressed as linear contrast of the three treatments. Special emphasis is paid to the combination of the three sources of multiplicity - multiple endpoints, multiple treatments, and two stages of the adaptive design. Particularly, the adaptation after the first stage comprises a change of the a-priori order of hypotheses.
Multivariate nonparametric tests of independence
2005
New test statistics are proposed for testing whether two random vectors are independent. Gieser and Randles, as well as Taskinen, Kankainen, and Oja have introduced and discussed multivariate extensions of the quadrant test of Blomqvist. This article serves as a sequel to this work and presents new multivariate extensions of Kendall's tau and Spearman's rho statistics. Two different approaches are discussed. First, interdirection proportions are used to estimate the cosines of angles between centered observation vectors and between differences of observation vectors. Second, covariances between affine-equivariant multivariate signs and ranks are used. The test statistics arising from these …