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24
Simultaneous Inference in General Parametric Models
, 2008
"... Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the prespecified significance level. Simultaneous inference procedures have to be used which ..."
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Cited by 211 (6 self)
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Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the prespecified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth of the results. For the analyses we use the R addon package multcomp, which provides a convenient interface to the general approach adopted here. Key words: multiple tests, multiple comparisons, simultaneous confidence intervals, adjusted pvalues, multivariate normal distribution, robust statistics. 1
The Maximum Approximate Composite Marginal Likelihood (MACML) Estimation of the Multinomial Probitbased Unordered Response Choice Models. Transportation Research Part B (forthcoming
, 2011
"... The likelihood functions of multinomial probit (MNP)based choice models entail the evaluation of analyticallyintractable integrals. As a result, such models are usually estimated using maximum simulated likelihood (MSL) techniques. Unfortunately, for many practical situations, the computational co ..."
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Cited by 18 (12 self)
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The likelihood functions of multinomial probit (MNP)based choice models entail the evaluation of analyticallyintractable integrals. As a result, such models are usually estimated using maximum simulated likelihood (MSL) techniques. Unfortunately, for many practical situations, the computational cost to ensure good asymptotic MSL estimator properties can be prohibitive and practically infeasible as the number of dimensions of integration rises. In this paper, we introduce a maximum approximate composite marginal likelihood (MACML) estimation approach for MNP models that can be applied using simple optimization software for likelihood estimation. It also represents a conceptually and pedagogically simpler procedure relative to simulation techniques, and has the advantage of substantial computational time efficiency relative to the MSL approach. The paper provides a “blueprint ” for the MACML estimation for a wide variety of MNP models.
Identification of Multivariate Outliers: A Performance Study
"... Abstract: Three methods for the identification of multivariate outliers (Rousseeuw ..."
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Cited by 5 (0 self)
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Abstract: Three methods for the identification of multivariate outliers (Rousseeuw
On multivariate t and Gauss probabilities in R
 R News
"... The numerical computation of a multivariate normal or t probability is often a difficult problem. Recent developments resulted in algorithms for the fast computation of those probabilities for arbitrary correlation structures. ..."
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Cited by 4 (2 self)
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The numerical computation of a multivariate normal or t probability is often a difficult problem. Recent developments resulted in algorithms for the fast computation of those probabilities for arbitrary correlation structures.
Parallel Computation of the Multivariate tDistribution
 Proceedings of the High Performance Computing Symposium 2001 (HPC’01
, 2001
"... We present a distributed algorithm for the computation of the multivariate tdistribution, based on quasiMonte Carlo (qmc) techniques, specically, lattice (Korobov) rules. Multiple comparison problems, as arise in statistical computations, are discussed as a sample application. Experimental results ..."
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Cited by 1 (0 self)
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We present a distributed algorithm for the computation of the multivariate tdistribution, based on quasiMonte Carlo (qmc) techniques, specically, lattice (Korobov) rules. Multiple comparison problems, as arise in statistical computations, are discussed as a sample application. Experimental results of our implementation show good speedup on a network of workstations. As the tdistribution integrals require transformation in order to apply the underlying qmc method, we propose a design for an interface with a transformation function library, which will also serve for dierent applications. We are merging the qmc techniques with our existing parallel adaptive integration package, ParInt. 1.
Usage
, 2010
"... Description Computes multivariate normal and t probabilities, quantiles, random deviates and densities. ..."
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Description Computes multivariate normal and t probabilities, quantiles, random deviates and densities.
Choose between two algorithms for evaluating normal distributions and define hyper parameters.
"... ..."
unknown title
, 2012
"... Empirical implementation of a quantitative reverse stress test for defaultable fixedincome instruments with macroeconomic factors and principal components ..."
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Empirical implementation of a quantitative reverse stress test for defaultable fixedincome instruments with macroeconomic factors and principal components