منابع مشابه
A Covariance Regression Model
Classical regression analysis relates the expectation of a response variable to a linear combination of explanatory variables. In this article, we propose a covariance regression model that parameterizes the covariance matrix of a multivariate response vector as a parsimonious quadratic function of explanatory variables. The approach can be seen as analogous to the mean regression model, and ha...
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Capturing predictor-dependent correlations amongst the elements of a multivariate response vector is fundamental to numerous applied domains, including neuroscience, epidemiology, and finance. Although there is a rich literature on methods for allowing the variance in a univariate regression model to vary with predictors, relatively little has been done in the multivariate case. As a motivating...
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Multivariate time series data arise in many applied domains, and it is often crucial to obtain a good characterization of how the covariance among the different variables changes over time. Certainly this is the case in financial applications in which covariance can change dramatically during times of financial crisis, revealing different associations among assets and countries than occur in a ...
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A nonlinear regression model with correlated, normally distributed stationary errors is investigated. Limit properties of an approximate estimator of an unknown covariance function of stationary errors are studied and suucient conditions under which this estimator is consistent are shown.
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The theory of estimation in a nonlinear regression model has been extensively studied by many authors (see Jennrich (1969), Rattkowsky (1983), Gallant (1987) and others). The main effort was devoted to the study of problems of estimation of unknown regression parameters by least squares method under the assumption that errors are independent and identically distributed with some unknown varianc...
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ژورنال
عنوان ژورنال: Statistica Sinica
سال: 2012
ISSN: 1017-0405
DOI: 10.5705/ss.2010.051