Simulation-Based Finite-Sample Normality Tests in Linear Regressions

In the literature on tests of normality, much concern has been expressed over the problems associated with residual-based procedures. Indeed, the specialized tables of critical points which are needed to perform the tests have been derived for the location-scale model; hence reliance on available significance points in the context of regression models may cause size distortions. We propose a general solution to the problem of controlling the size normality tests for the disturbances of standard linear regression, which is based on using the technique of Monte Carlo tests.

Simulation-Based Finite-and Large-sample Inference Methods in Multivariate Regressions and Seemingly Unrelated Regressions

In the context of multivariate regression (MLR) and seemingly unrelated regressions (SURE) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. in this paper, we propose finite-and large-sample likelihood-based test procedures for possibly non-linear hypotheses on the coefficients of MLR and SURE systems.

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.

Simulation of the postoperative trunk appearance in scoliosis surgery

Persistence of external trunk asymmetry after scoliosis surgical treatment is frequent and difficult to predict by clinicians. This is a significant problem considering that correction of the apparent deformity is a major factor of satisfaction for the patients. A simulation of the correction on the external appearance would allow the clinician to illustrate to the patient the potential result of the surgery and would help in deciding on a surgical strategy that could most improve his/her appearance. We describe a method to predict the scoliotic trunk shape after a spine surgical intervention. The capability of our method was evaluated using real data of scoliotic patients. Results of the qualitative evaluation were very promising and a quantitative evaluation based on the comparison of the simulated and the actual postoperative trunk surface showed an adequate accuracy for clinical assessment. The required short simulation time also makes our approach an eligible candidate for a clinical environment demanding interactive simulations.

Modeling premartensitic effects in Ni2MnGa: A mean-field and Monte Carlo simulation study

ic first-order transition line ending in a critical point. This critical point is responsible for the existence of large premartensitic fluctuations which manifest as broad peaks in the specific heat, not always associated with a true phase transition. The main conclusion is that premartensitic effects result from the interplay between the softness of the anomalous phonon driving the modulation and the magnetoelastic coupling. In particular, the premartensitic transition occurs when such coupling is strong enough to freeze the involved mode phonon. The implication of the results in relation to the available experimental data is discussed.

Measurement and simulation of anisotropy in the Infrared and Raman spectra of beta-FeSi2 single crystals

In this paper we show that the orthorhombic phase of FeSi2 (stable at room temperature) displays a sizable anisotropy in the infrared spectra, with minor effects in the Raman data too. This fact is not trivial at all, since the crystal structure corresponds to a moderate distortion of the fluorite symmetry. Our analysis is carried out on small single crystals grown by flux transport, through polarization-resolved far-infrared reflectivity and Raman measurements. Their interpretation has been obtained by means of the simulated spectra with tight-binding molecular dynamics.

Diffusive transport properties in monovalent and divalent metal-ion halide melts: A computer simulation study

Self- and cross-velocity correlation functions and related transport coefficients of molten salts are studied by molecular-dynamics simulation. Six representative systems are considered, i.e., NaCl and KCl alkali halides, CuCl and CuBr noble-metal halides, and SrCl2 and ZnCl2 divalent metal-ion halides. Computer simulation results are compared with experimental self-diffusion coefficients and electrical conductivities. Special attention is paid to dynamic cross correlations and their dependence on the Coulomb interactions as well as on the size and mass differences between anions and cations.

Einführung in die stochastische Simulation mit Fathom

Die "Einführung in die stochastische Simulation mit Fathom" führt in die Simulation mit der Computersoftware Fathom ein und entstand im Rahmen meines Dissertationsprojekts. Voraussetzung sind elementare Grundkenntnisse in dieser Software, z.B. für die Analyse von eindimensionalen Daten. Die Arbeit basiert auf einer ausführlichen Analyse stochastischer Situationen, die als Zufallsexperimente modelliert werden können. Sie wurde ursprünglich für Studenten der Veranstaltung "Elementare Stochastik" an der Universität Kassel didaktisch aufbereitet. Sie ist aber auch für alle Lehrenden und Lernenden an Schule und Hochschule gedacht, die die stochastische Simulation mit Fathom erlernen wollen. Das Dokument dient dazu, eine systematische Anleitung zum Erstellen eigener Simulationen zu geben. Eine didaktische Hilfestellung bietet dazu das dreigegliederte Simulationskonzept: 1. Festlegen des stochastischen Modell, 2. Erstellen eines Simulationsplans und 3. Realisierung in FATHOM. Verschiedene Simulationsarten werden ausführlich an einem Beispiel vorgestellt und können in Arbeitsumgebungen selbst erzeugt werden. Die Erstellung der Arbeit wurde mit Mitteln des BLK-Projektes Netzwerk Wissenschaftliche Weiterbildung für Lehramtsberufe (NWWL) an der Universität Kassel gefördert.

Simulation on the process of fatigue crack initiation in a martensitic stainless steel

Die vorliegende Arbeit beschäftigt sich mit der Computersimulation des Rissinitiierungsprozesses für einen martensitischen Stahl, der der niederzyklischen Ermüdung unterworfen wurde. Wie auf der Probenoberfläche beobachtet wurde, sind die Initiierung und das frühe Wachstum dieser Mikrorisse in hohem Grade von der Mikrostruktur abhängig. Diese Tatsache wurde in mesoskopischen Schädigungsmodellen beschrieben, wobei die Körner als einzelne Kristalle mit anisotropem Materialverhalten modelliert wurden. Das repräsentative Volumenelement (RVE), das durch einen Voronoi-Zerlegung erzeugt wurde, wurde benutzt, um die Mikrostruktur des polykristallinen Materials zu simulieren. Spannungsverteilungen wurden mit Hilfe der Finiten-Elemente-Methode mit elastischen und elastoplastischen Materialeigenschaften analysiert. Dazu wurde die Simulation zunächst an zweidimensionalen Modellen durchgeführt. Ferner wurde ein vereinfachtes dreidimensionales RVE hinsichtlich des sowohl dreidimensionalen Gleitsystems als auch Spannungszustandes verwendet. Die kontinuierliche Rissinitiierung wurde simuliert, indem der Risspfad innerhalb jedes Kornes definiert wurde. Die Zyklenanzahl bis zur Rissinitiierung wurde auf Grundlage der Tanaka-Mura- und Chan-Gleichungen ermittelt. Die Simulation lässt auf die Flächendichten der einsegmentige Risse in Relation zur Zyklenanzahl schließen. Die Resultate wurden mit experimentellen Daten verglichen. Für alle Belastungsdehnungen sind die Simulationsergebnisse mit denen der experimentellen Daten vergleichbar.

The Kineticist's Workbench: Combining Symbolic and Numerical Methods in the Simulation of Chemical Reaction Mechanisms

The Kineticist's Workbench is a program that simulates chemical reaction mechanisms by predicting, generating, and interpreting numerical data. Prior to simulation, it analyzes a given mechanism to predict that mechanism's behavior; it then simulates the mechanism numerically; and afterward, it interprets and summarizes the data it has generated. In performing these tasks, the Workbench uses a variety of techniques: graph- theoretic algorithms (for analyzing mechanisms), traditional numerical simulation methods, and algorithms that examine simulation results and reinterpret them in qualitative terms. The Workbench thus serves as a prototype for a new class of scientific computational tools---tools that provide symbiotic collaborations between qualitative and quantitative methods.