Bayes Sequential Estimation Procedures in Exponential-Type Processes For a Polynomial Cost Function

Statistics ◽  
1995 ◽  
Vol 26 (1) ◽  
pp. 61-73
Author(s):  
Ryszard Magiera
Keyword(s):  
Cost Function ◽  
Exponential Type ◽  
Sequential Estimation ◽  
Bayes Sequential Estimation ◽  
Estimation Procedures

On the moments of some first-passage times

1985 ◽  
Vol 22 (02) ◽  
pp. 461-466
Author(s):  
Valeri T. Stefanov
Keyword(s):  
Continuous Function ◽  
Exponential Type ◽  
First Passage Time ◽  
Sequential Estimation ◽  
Passage Time ◽  
Positive Continuous Function ◽  
First Passage ◽  
Finite Dimensional ◽  
Independent Increments ◽  
Passage Times

Let {Xt } t≧0 (t may be discrete or continuous) be a random process whose finite-dimensional distributions are of exponential type. The first-passage time inf{t:Xt ≧f(t)}, where f(t) is a positive, continuous function, such that f(t)= o(t) as t↑∞, is considered. The problem of finiteness of its moments is solved for both the case that {Xt } t≧0 has stationary independent increments as well as the case in which no assumptions are made about stationarity and independence for the increments of the process. Applications to sequential estimation are also given.

scholarly journals Decision Makers' Ability To Identify Unusual Costs And Implications For Alternative Estimation Procedures

2011 ◽  
Vol 7 (4) ◽  
pp. 36
Author(s):  
MaryAnne Atkinson ◽  
Scott Jones
Keyword(s):  
Cost Function ◽  
Least Squares ◽  
Ordinary Least Squares ◽  
Decision Makers ◽  
Data Set ◽  
Influential Points ◽  
Estimation Procedures ◽  
Data Points ◽  
Weighting Rule

This paper reports the results of an experiment in which individuals visually fitted a cost function to data. The inclusion or omission of unusual data points within the data set was experimentally manipulated. The results indicate that individuals omit outliers from their visual fits, but do not omit influential points. Evidence also suggests that the weighting rule used by individuals is more robust that the weighting rule used in the ordinary least squares criterion.

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