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ON THE STABILITY OF A HETEROSCEDASTIC PROCESS
Journal of Time Series Analysis, 1986Abstract. In this paper we derive the stability conditions in a time series regression model with a particular form of conditional heteroscedasticity. The variables affecting the variance include lagged errors, lagged dependent variables and a forecast variable.
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Heteroscedastic factor mixture analysis
Statistical Modelling, 2010When data come from an unobserved heterogeneous population, common factor analysis is not appropriate to estimate the underlying constructs of interest. By replacing the traditional assumption of Gaussian distributed factors by a finite mixture of multivariate Gaussians, the unobserved heterogeneity can be modelled by latent classes.
MONTANARI, ANGELA, VIROLI, CINZIA
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Estimation of Heteroscedastic Multilinear Systems
2020 59th IEEE Conference on Decision and Control (CDC), 2020In this paper, we propose an estimation method for heteroscedastic multilinear systems. The system consists of a multilinear map of latent functions and an input-dependent noise process. We assume Gaussian-process priors on the unknowns to embed non-parametric models.
Mingliang Wang +3 more
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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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Evidence of Heteroscedasticity in the Market Model
The Journal of Business, 1975The market model has gained wide acceptance among academicians and is becoming increasingly accepted by portfolio managers. This study presents a statistical analysis of the homoscedasticity assumption, which, although important to the successful application of the market model, has not been fully tested in the literature.
Martin, John D, Klemkosky, Robert C
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Probabilistic PCA for Heteroscedastic Data
2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019Principal Component Analysis (PCA) is a standard dimensionality reduction technique, but it treats all samples uniformly’ making it suboptimal for heterogeneous data that are increasingly common in modern settings. This paper proposes a PCA variant for samples with heterogeneous noise levels, i.e., heteroscedastic noise, that naturally arise when some ...
David Hong +2 more
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A note on studentizing a test for heteroscedasticity
Journal of Econometrics, 1981Abstract Breusch and Pagan (1979) have recently proposed a convenient test for heteroscedasticity in general linear models. This note derives the asymptotic distribution of their test under sequences of contiguous alternatives to the null hypothesis of homoscedasticity.
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Heteroscedastic Nonlinear Regression Models
Communications in Statistics - Simulation and Computation, 2010In this article, we present a generalization of the Bayesian methodology introduced by Cepeda and Gamerman (2001) for modeling variance heterogeneity in normal regression models where we have orthogonality between mean and variance parameters to the general case considering both linear and highly nonlinear regression models. Under the Bayesian paradigm,
Edilberto Cepeda Cuervo +1 more
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Heteroscedasticity in the Market Model: A Comment
The Journal of Business, 1977A well-known assumption of the standard linear regression model, yt = a + Ixt + et, t = 1, ..., T, is that the variance of the zero mean error component, et, is constant over t, var et = o2 = o-.1 In a recent paper, Martin and Klemkosky propose that a measure of the departure from this assumption of homoscedasticity for a "market model," Rt = 5i ...
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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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