Sequential risk-efficient estimation of the parameter in the uniform density.
Sequential Simultaneous Estimation of the Mean and Variance: The Rate of Convergence.
Sequential statistical structures
Simultaneous Estimation After Selection and Ranking and Other Procedures: The Negative Exponential Case.
Some investigations in minimax estimation theory [Book]
Some parameter estimation problems for hypoelliptic homogeneous Gaussian diffusions
Special sequential estimation problems in Markov processes
The Bayes sequential estimation of a normal mean from delayed observations
The problem of estimating the mean of a normal distribution is considered in the special case when the data arrive at random times. Certain classes of Bayes sequential estimation procedures are derived under LINEX and reflected normal loss function and with the observation cost determined by a function of the stopping time and the number of observations up to this time.
The Bayesian sequential model with the range-based probability estimation
The characterization of efficient sequential plans for the Ornstein-Uhlenbeck velocity process
The recursive identification problem in control and signal processing applications
The stability of parameter estimation of fuzzy variables
Recently, the parameter estimations for normal fuzzy variables in the Nahmias’ sense was studied by Cai [4]. These estimates were also studied for general -related, but not necessarily normal fuzzy variables by Hong [10] In this paper, we report on some properties of estimators that would appear to be desirable, including unbiasedness. We also consider asymptotic or “large-sample” properties of a particular type of estimator.
Two-Stage Point Estimation with a Shrinkage Stopping Rule.
Uniform asymptotic normality for the Bernoulli scheme
It is easy to notice that no sequence of estimators of the probability of success θ in a Bernoulli scheme can converge (when standardized) to N(0,1) uniformly in θ ∈ ]0,1[. We show that the uniform asymptotic normality can be achieved if we allow the sample size, that is, the number of Bernoulli trials, to be chosen sequentially.
Γ-minimax sequential estimation for Markov-additive processes
The problem of estimating unknown parameters of Markov-additive processes from data observed up to a random stopping time is considered. To the problem of estimation, the intermediate approach between the Bayes and the minimax principle is applied in which it is assumed that a vague prior information on the distribution of the unknown parameters is available. The loss in estimating is assumed to consist of the error of estimation (defined by a weighted squared loss function) as well as a cost of...
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