Results 211 to 220 of about 99,233 (263)

Variance Decomposition Analysis for Nonlinear Economic Models1

Oxford Bulletin of Economics and Statistics, 2020
AbstractIn this paper, we propose a new method called the total variance method and algorithms to compute and analyse variance decomposition for nonlinear economic models. We provide theoretical and empirical examples to compare our method with the only existing method called generalized forecast error variance decomposition (GFEVD).
Maksim Isakin, Phuong V. Ngo
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A Bayesian analysis of a variance decomposition for stock returns

Journal of Empirical Finance, 2002
We apply Bayesian methods to study a common VAR-based approach for decomposing the variance of excess stock returns into components reflecting news about future excess stock returns, future real interest rates, and future dividends. We develop a new prior elicitation strategy which involves expressing beliefs about the components of the variance ...
Burton Hollifield, Kai Li, Gary M. Koop
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Multiclass Laplacian support vector machine with functional analysis of variance decomposition

Computational Statistics & Data Analysis, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Beomjin Park, Changyi Park
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A Variance Decomposition Analysis of the Housing Bubble

Journal of Real Estate Portfolio Management, 2013
Executive Summary. This study uses a variance ratio procedure derived from the Campbell-Shiller return decomposition to test for evidence of a bubble in housing returns for the period 1997 to 2007....
Jeffery Bredthauer, John Geppert
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Estimating Mean Dimensionality of Analysis of Variance Decompositions

Journal of the American Statistical Association, 2006
Analysis of variance (ANOVA) is now often applied to functions defined on the unit cube, where it serves as a tool for the exploratory analysis of functions. The mean dimension of a function, defined as a natural weighted combination of its ANOVA mean squares, provides one measure of how hard or easy it is to integrate the function by quasi-Monte Carlo
Liu, Ruixue, Owen, Art B.
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Variance decomposition-based sensitivity analysis via neural networks

Reliability Engineering & System Safety, 2003
Abstract This paper illustrates a method for efficiently performing multiparametric sensitivity analyses of the reliability model of a given system. These analyses are of great importance for the identification of critical components in highly hazardous plants, such as the nuclear or chemical ones, thus providing significant insights for their risk ...
MARSEGUERRA, MARZIO, R. MASINI, ZIO, ENRICO, G. COJAZZI   +3 more
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Analysis of the Stochasticity of Mortality Using Variance Decomposition

2014
We analyse the stochasticity in mortality data from the USA, the UK and Sweden, and in particular to which extent mortality rates are explained by systematic variation, due to various risk factors, and random noise. We formalise this in terms of a mixed regression model with a logistic link function, and decompose the variance of the observations into ...
Erland Ekheden, Ola Hössjer
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Model uncertainty analysis by variance decomposition

Physics and Chemistry of the Earth, Parts A/B/C, 2012
Abstract Errors and uncertainties in hydrological, hydraulic and environmental models are often substantial. In good modelling practice, they are quantified in order to supply decision-makers with important additional information on model limitations and sources of uncertainty. Several uncertainty analysis methods exist, often with various underlying
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CHOLESKY DECOMPOSITION OF A VARIANCE MATRIX IN REPEATED MEASURES ANALYSIS

Australian Journal of Statistics, 1988
SummaryThe Cholesky decomposition is given for the inverse of a variance matrix occurring in repeated measures problems where observations have a correlation structure both within and between experimental units. The use of this decomposition is outlined for ML and REML estimation procedures.
S. Lianto, C.A. McGilchrist
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Analysis of variance for ‘component stripping’ decomposition of multiexponential curves

Computer Methods and Programs in Biomedicine, 1993
An extended analysis of variance is presented for a multiexponential curve fitting procedure, known as 'curve stripping', 'curve peeling', or 'successive subtraction'. In addition to the standard, single variable curves, this analysis includes the two dimensional multiexponential surface analysis. The calculations take into account features required by
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