A GLR Control Chart for Monitoring the Mean Vector of a Multivariate Normal Process

2013 ◽  
Vol 45 (1) ◽  
pp. 18-33 ◽  
Author(s):  
Sai Wang ◽  
Marion R. Reynolds
Keyword(s):  
Control Chart ◽  
Normal Process ◽  
Multivariate Normal ◽  
The Mean ◽  
Mean Vector

On Sequential Procedures for the Point Estimation of the Mean Vector

1988 ◽  
Vol 37 (1-2) ◽  
pp. 47-54 ◽  
Author(s):  
R. Karan Singh ◽  
Ajit Chaturvedi
Keyword(s):  
Normal Population ◽  
Stopping Time ◽  
Point Estimation ◽  
Multivariate Normal ◽  
Minimum Risk ◽  
Sequential Procedures ◽  
Multivariate Normal Population ◽  
Bounded Risk ◽  
The Mean ◽  
Mean Vector

Sequential procedures are proposed for (a) the minimum risk point estimation and (b) the bounded risk point estimation of the mean vector of a multivariate normal population . Second-order approximations are derived. For the problem (b), a lower bound for the number of additional observations (after stopping time) is obtained which ensures “ exact” boundedness of the risk associated witb the sequential procedure.

scholarly journals On sequential estimation of a certain estimable function of the mean vector of a multivariate normal distribution

1973 ◽  
Vol 15 (3) ◽  
pp. 291-295 ◽  
Author(s):  
V. K. Rohatgi ◽  
Suresh C. Rastogi
Keyword(s):  
Normal Distribution ◽  
Multivariate Normal Distribution ◽  
Sequential Estimation ◽  
Stopping Rule ◽  
Diagonal Matrix ◽  
Multivariate Normal ◽  
The Mean ◽  
Mean Vector

Consider a k-variable normal distribution Ν (μ,Σ where mgr; = (μ1,μ2, … μk)' and Σ is diagonal matrix of unknown elements >0,i = 1,2, … k. The problem of sequential estimation of = 1 αiμi is considered. The stopping rule is shown to have some interesting limiting properties when the σi's become infinite.

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