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Author: Michael Mohr
Publisher:
ISBN:
Category :
Languages : de
Pages : 25
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Book Description
Author: Michael Mohr
Publisher:
ISBN:
Category :
Languages : de
Pages : 25
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Book Description
Author: Michael Mohr
Publisher:
ISBN:
Category :
Languages : en
Pages : 107
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Author: Norbert Christopeit
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Category :
Languages : de
Pages : 14
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Author: Stanford University. Department of Statistics
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Category :
Languages : en
Pages : 12
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Book Description
Author: Denzil G. Fiebig
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Category : Least squares
Languages : en
Pages : 44
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Author: Jeremy Sin-hing Wu
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ISBN:
Category :
Languages : en
Pages : 246
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Author: Robert Ernest Tarone
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Category :
Languages : en
Pages : 190
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Author: P. K. Bhattacharya (Mathematician)
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Category : Matrices
Languages : en
Pages : 32
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For the linear regression of y on x observations the loss in estimating the true regression function by another function is considered as a loss function. For the loss function, it is shown under certain conditions that if the class of estimates which are linear in y's and have bounded risk is non-empty, then the estimate obtained by the method of least squares belongs to this class and has uniformly minimum risk in this class. A necessary and sufficient condition on the distribution function of x observations is obtained for this class to be non-empty, which unfortunately is not easy to verify in particular cases and is violated in a ver simple situation. owever, by a sequential modification of the sampling scheme, this condition may always be satisfied at the cost of an arbitrarily small increase in the expected sa ple size. I T IS ALSO SHOWN UNDER CERTAIN FURTHER C NDITIONS ON THE FAMILY OF ADMISSIBLE DISTRIB TIONS THAT THE LEAST SQUARES ESTIMATOR IS MINIMAX IN THE CLASS OF ALL ESTIMATORS. (Author).
Author: Virendra K. Srivastava
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ISBN:
Category : Econometrics
Languages : en
Pages : 18
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Author: Stanford University. Department of Statistics
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ISBN:
Category : Time-series analysis
Languages : en
Pages : 318
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The author considers estimation procedures for the moving average model of order q. Walker's method uses k sample autocovariances (k> or = q). Assume that k depends on T in such a way that k nears infinity as T nears infinity. The estimates are consistent, asymptotically normal and asymptotically efficient if k = k (T) dominates log T and is dominated by (T sub 1/2). The approach in proving these theorems involves obtaining an explicit form for the components of the inverse of a symmetric matrix with equal elements along its five central diagonals, and zeroes elsewhere. The asymptotic normality follows from a central limit theorem for normalized sums of random variables that are dependent of order k, where k tends to infinity with T. An alternative form of the estimator facilitates the calculations and the analysis of the role of k, without changing the asymptotic properties.
