Results 231 to 240 of about 104,612 (287)
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The Accounting Review, 2006
Using the Vuolteenaho (2002) variance decomposition methodology, this study assesses the relative value relevance of cash flow, accrual, and expected return news on SEC and preliminary earnings filing dates, as measured by their contribution to the volatility of unexpected returns.
Jeffrey L. Callen +2 more
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Using the Vuolteenaho (2002) variance decomposition methodology, this study assesses the relative value relevance of cash flow, accrual, and expected return news on SEC and preliminary earnings filing dates, as measured by their contribution to the volatility of unexpected returns.
Jeffrey L. Callen +2 more
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Structured multicategory support vector machines with analysis of variance decomposition
Biometrika, 2006The support vector machine has been a popular choice of classification method for many applications in machine learning. While it often outperforms other methods in terms of classification accuracy, the implicit nature of its solution renders the support vector machine less attractive in providing insights into the relationship between covariates and ...
Yoonkyung Lee +3 more
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, 2020
This paper proposes a quantile variance decomposition framework for measuring extreme risk spillover effects across international stock markets. The framework extends the spillover index approach suggested by Diebold and Yilmaz (2009) using a quantile ...
Xianfang Su
semanticscholar +1 more source
This paper proposes a quantile variance decomposition framework for measuring extreme risk spillover effects across international stock markets. The framework extends the spillover index approach suggested by Diebold and Yilmaz (2009) using a quantile ...
Xianfang Su
semanticscholar +1 more source
How Much Does Business Model Matter for Firm Performance? A Variance Decomposition Analysis
, 2020Although an emerging literature has described the phenomenon of business model, little is known about how much business model matters in explaining heterogeneity in business performance.
Timo Sohl, G. Vroom, Markus Fitza
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A variance decomposition approach to the analysis of genetic algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation, 2013Prediction of the evolutionary process is a long standing problem both in the theory of evolutionary biology and evolutionary computation (EC). It has long been realized that heritable variation is crucial to both the response to selection and the success of genetic algorithms.
Paixão, Tiago; Barton, Nicholas H
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Multiclass Laplacian support vector machine with functional analysis of variance decomposition
Computational Statistics & Data Analysis, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Beomjin Park, Changyi Park
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Estimating Mean Dimensionality of Analysis of Variance Decompositions
Journal of the American Statistical Association, 2006Analysis 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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International Review of Economics and Finance, 2021
We propose multilayer information spillover networks, including return spillover layer, volatility spillover layer, and extreme risk spillover layer in the variance decomposition framework for comprehensively investigating the information spillovers and ...
Gangjin Wang +4 more
semanticscholar +1 more source
We propose multilayer information spillover networks, including return spillover layer, volatility spillover layer, and extreme risk spillover layer in the variance decomposition framework for comprehensively investigating the information spillovers and ...
Gangjin Wang +4 more
semanticscholar +1 more source
CHOLESKY DECOMPOSITION OF A VARIANCE MATRIX IN REPEATED MEASURES ANALYSIS
Australian Journal of Statistics, 1988SummaryThe 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, 1993An 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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