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Robust Asymptotic Statistics: Volume I / Edition 1

Robust Asymptotic Statistics: Volume I / Edition 1

Current price: $119.99
CartBuy Online
Robust Asymptotic Statistics: Volume I / Edition 1

Barnes and Noble

Robust Asymptotic Statistics: Volume I / Edition 1

Current price: $119.99
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Size: OS

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1 To the king, my lord, from your servant Balasi : 2 ... The king should have a look. Maybe the scribe who reads to the king did not understand . . . . shall I personally show, with this tablet that I am sending to the king, my lord, how the omen was written. 3 Really, he who has not followed the text with his finger cannot possibly understand it. This book is about optimally robust functionals and their unbiased esti­ mators and tests. Functionals extend the parameter of the assumed ideal center model to neighborhoods of this model that contain the actual distri­ bution. The two principal questions are (F): Which functional to choose? and (P): Which statistical procedure to use for the selected functional? Using a local asymptotic framework, we deal with both problems by linking up nonparametric statistical optimality with infinitesimal robust­ ness criteria. Thus, seemingly separate developments in robust statistics are presented in a unifying way.
1 To the king, my lord, from your servant Balasi : 2 ... The king should have a look. Maybe the scribe who reads to the king did not understand . . . . shall I personally show, with this tablet that I am sending to the king, my lord, how the omen was written. 3 Really, he who has not followed the text with his finger cannot possibly understand it. This book is about optimally robust functionals and their unbiased esti­ mators and tests. Functionals extend the parameter of the assumed ideal center model to neighborhoods of this model that contain the actual distri­ bution. The two principal questions are (F): Which functional to choose? and (P): Which statistical procedure to use for the selected functional? Using a local asymptotic framework, we deal with both problems by linking up nonparametric statistical optimality with infinitesimal robust­ ness criteria. Thus, seemingly separate developments in robust statistics are presented in a unifying way.

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