Statistical Inference in Stochastic Processes
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advanced statistical methods
AIC Criterion
AIC Statistic
Asymptotic Risk
Atypical Orientation
Category=PBT
Cumulative Hazard Rate
Discrete Time Linear Stochastic Systems
edge detection algorithms
Empirical Bayes Estimator
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Gaussian Likelihood
image analysis
inference for diffusion processes with jumps
Kaiman Filter
Kalman-Bucy linear system
Kernel Estimator
Linear Volterra Integral Equation
Log Likelihood Function
Markov Random Field
Maximum Likelihood Estimator
Missing Values
Nonparametric Maximum Likelihood Estimators
probabalistic models
probability theory
Rainfall Fields
Shrinkage Estimator
Skorohod Space
state-space models
statistical techniques
Stein's Lemma
Stein’s Lemma
stochastic inference
Stochastic Integrals
stochastic modeling
survival data analysis
Type Token Relationship
Unique Stationary Solution
Volterra Integral Equation
Weak Convergence
Product details
- ISBN 9780824784171
- Weight: 498g
- Dimensions: 152 x 229mm
- Publication Date: 18 Dec 1990
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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Covering both theory and applications, this collection of eleven contributed papers surveys the role of probabilistic models and statistical techniques in image analysis and processing, develops likelihood methods for inference about parameters that determine the drift and the jump mechanism of a di
N. U. Prabhu, I. V. Basawa
