Semimartingales and their Statistical Inference

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A01=B.L.S. Prakasa Rao
advanced statistics
asymptotic analysis
Author_B.L.S. Prakasa Rao
Category=PBT
Category=PBWH
Category=PBWL
Compensated Poisson Process
Compound Poisson Process
Conditional Expectation
Continuous Local Martingale
diffusion processes
EPSP
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Excitatory Post-synaptic Potential
Exponential Family
Fa Mily
Galton Watson Branching Process
inference for stochastic processes
Inhibitory Post-synaptic Potential
IPSP
Levy Process
Local Martingale
Normal Variance Mixture
Post-synaptic Potential
probability theory
Pure Birth Process
Quasi-score Function
statistical modeling
Stochastic Differential Equation
stochastic modeling applications
Stochastic Point Processes
Toeplitz Lemma
Unique Maximum Likelihood Estimator
Vector Martingale

Product details

  • ISBN 9781584880080
  • Weight: 1160g
  • Dimensions: 152 x 229mm
  • Publication Date: 11 May 1999
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Statistical inference carries great significance in model building from both the theoretical and the applications points of view. Its applications to engineering and economic systems, financial economics, and the biological and medical sciences have made statistical inference for stochastic processes a well-recognized and important branch of statistics and probability.
The class of semimartingales includes a large class of stochastic processes, including diffusion type processes, point processes, and diffusion type processes with jumps, widely used for stochastic modeling. Until now, however, researchers have had no single reference that collected the research conducted on the asymptotic theory for semimartingales.

Semimartingales and their Statistical Inference, fills this need by presenting a comprehensive discussion of the asymptotic theory of semimartingales at a level needed for researchers working in the area of statistical inference for stochastic processes. The author brings together into one volume the state-of-the-art in the inferential aspect for such processes. The topics discussed include:

  • Asymptotic likelihood theory
  • Quasi-likelihood
  • Likelihood and efficiency
  • Inference for counting processes
  • Inference for semimartingale regression models

    The author addresses a number of stochastic modeling applications from engineering, economic systems, financial economics, and medical sciences. He also includes some of the new and challenging statistical and probabilistic problems facing today's active researchers working in the area of inference for stochastic processes.
  • Rao\, B.L.S. Prakasa

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