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Gaussian Process-Mixture Conditional Heteroscedasticity
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014Generalized autoregressive conditional heteroscedasticity (GARCH) models have long been considered as one of the most successful families of approaches for volatility modeling in financial return series. In this paper, we propose an alternative approach based on methodologies widely used in the field of statistical machine learning.
Platanios, Emmanouil Antonios +1 more
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Heteroscedasticity and nonnormality
Communications in Partial Differential Equations, 1989In this paper we consider the problem of comparing several means under heteroscedasticity and nonnormality. By combining Huber's M-estimators with the Brown-Forsythe test , several robust procedures were developed; these procedures were compared through computer simulation studies with-the Tan-Tabatabai procedure which was developed by combining Tiku's
W. Y. Tan, M. A. Tabatabai
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Stock Prices and Heteroscedasticity
The Journal of Business, 1976This paper provides evidence that the variance of returns on common stocks is not constant through time but is related to the volume of shares traded. In other words, returns on stocks are heteroscedastic. The work extends the approaches of Osborne, Granger and Morgenstern, and Clark.' Distributions of returns are known to be leptokurtic.
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Heteroscedastic Nonlinear Regression
Technometrics, 1988Several parameter estimation methods for dealing with heteroscedasticity in nonlinear regression are described. These include variations on ordinary, weighted, iteratively reweighted, extended. and generalized least squares. Some of these variations are new, and one of them in particular, modified extended iteratively reweighted least squares (MEIRLS),
S. L. Beal, L. B. Sheiner
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DOA Estimation in heteroscedastic noise
Signal Processing, 2019The paper considers direction of arrival (DOA) estimation from long-term observations in a very noisy environment. The concern is to derive methods obtaining reasonable DOAs at very low SNR. The noise is assumed zero-mean Gaussian and its variance varies in time and space, causing stationary data models to fit poorly over long observation times ...
Peter Gerstoft +3 more
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Heteroscedastic transformation cure regression models.
Statistics in medicine, 2016Cure models have been applied to analyze clinical trials with cures and age-at-onset studies with nonsusceptibility. Lu and Ying (On semiparametric transformation cure model. Biometrika 2004; 91:331?-343. DOI: 10.1093/biomet/91.2.331) developed a general class of semiparametric transformation cure models, which assumes that the failure times of uncured
Chen, Chyong-Mei, Chen, Chen-Hsin
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2013
In this chapter, we introduce three fiducial approaches to heteroscedastic ANOVA and MANOVA. The first approach is that of Li et al. (2011) which was proposed for ANOVA but can be easily generalized to MANOVA. The second approach is that implicit in Behrens (Landw. Jb. 68, 807–837, 1929) paper. The third approach is that implicit in Fisher (Ann. Eugen.
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In this chapter, we introduce three fiducial approaches to heteroscedastic ANOVA and MANOVA. The first approach is that of Li et al. (2011) which was proposed for ANOVA but can be easily generalized to MANOVA. The second approach is that implicit in Behrens (Landw. Jb. 68, 807–837, 1929) paper. The third approach is that implicit in Fisher (Ann. Eugen.
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Interpreting Heteroscedasticity
American Journal of Political Science, 1979George W. Downs, David M. Rocke
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Educational and Psychological Measurement, 1967
Edwin E. Ghiselli, Eric P. Sanders
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Edwin E. Ghiselli, Eric P. Sanders
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