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Weak Convergence of Stochastic Processes
Weak Convergence of Stochastic Processes
Knygos.lt klubas Knygos.lt nariams
144,92 €
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The purpose of this book is to present results on the subject of weak convergence in function spaces to study invariance principles in statistical applications to dependent random variables, U-statistics, censor data analysis. Different techniques, formerly available only in a broad range of literature, are for the first time presented here in a self-contained fashion. Contents: Weak convergence of stochastic processesWeak convergence in metric spacesWeak convergence on C[0, 1] and D[0,∞…
  • Leidėjas:
  • Metai: 2016
  • Puslapiai: 148
  • ISBN-10: 3110475421
  • ISBN-13: 9783110475425
  • Formatas: 17 x 23.9 x 1 cm, minkšti viršeliai
  • Kalba: Anglų

Weak Convergence of Stochastic Processes (el. knyga) (skaityta knyga) | knygos.lt

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Aprašymas

The purpose of this book is to present results on the subject of weak convergence in function spaces to study invariance principles in statistical applications to dependent random variables, U-statistics, censor data analysis. Different techniques, formerly available only in a broad range of literature, are for the first time presented here in a self-contained fashion.

Contents:
Weak convergence of stochastic processes
Weak convergence in metric spaces
Weak convergence on C[0, 1] and D[0,∞)
Central limit theorem for semi-martingales and applications
Central limit theorems for dependent random variables
Empirical process
Bibliography

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Daugiau
  • Autorius: Vidyadhar S Mandrekar
  • Leidėjas:
  • Metai: 2016
  • Puslapiai: 148
  • ISBN-10: 3110475421
  • ISBN-13: 9783110475425
  • Formatas: 17 x 23.9 x 1 cm, minkšti viršeliai
  • Kalba: Anglų

The purpose of this book is to present results on the subject of weak convergence in function spaces to study invariance principles in statistical applications to dependent random variables, U-statistics, censor data analysis. Different techniques, formerly available only in a broad range of literature, are for the first time presented here in a self-contained fashion.

Contents:
Weak convergence of stochastic processes
Weak convergence in metric spaces
Weak convergence on C[0, 1] and D[0,∞)
Central limit theorem for semi-martingales and applications
Central limit theorems for dependent random variables
Empirical process
Bibliography

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