scholarly journals The optimum decision rules for the oddity task

1996 ◽  
Vol 58 (1) ◽  
pp. 10-21 ◽  
Author(s):  
Niek J. Versfeld ◽  
Huanping Dai ◽  
David M. Green
Keyword(s):  
Decision Rules ◽  
Oddity Task ◽  
Optimum Decision

scholarly journals The optimum decision rules in thesame-different paradigm

1996 ◽  
Vol 58 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Huanping Dai ◽  
Niek J. Versfeld ◽  
David M. Green
Keyword(s):  
Decision Rules ◽  
Optimum Decision

Optimal decision rules for the oddity task

1994 ◽  
Vol 95 (5) ◽  
pp. 2944-2944
Author(s):  
Niek J. Versfeld ◽  
Huanping Dai ◽  
David M. Green
Keyword(s):  
Decision Rules ◽  
Optimal Decision ◽  
Oddity Task

Optimum probabilistic processing in colour perception. I. Colour discrimination

1979 ◽  
Vol 205 (1159) ◽  
pp. 229-247 ◽  
Keyword(s):  
Statistical Estimation ◽  
Decision Rules ◽  
Colour Space ◽  
Spectral Parameters ◽  
Colour Perception ◽  
Optimum Decision ◽  
Statistical Estimation Theory ◽  
Wavelength Discrimination ◽  
Geometrical Concepts ◽  
Element Theory

A comprehensive account of wavelength discrimination and colour satu­ration discrimination is given in terms of optimum probabilistic signal detection. The theory is a logical deduction from statistical estimation theory of the visual estimate of the spectral parameters of the stimulus. In place of geometrical concepts associated with colour-space geometry, stimulus discriminability is determined by optimum decision rules given by likelihood ratio tests on statistics that are postulated for the trichro­matic responses. The classical line element theory and its formulations are deduced to be discriminability measures between signals. The different mathematical forms of classical theory are shown to correspond to differ­ent statistical constraints.

scholarly journals Estimating Discrete Parameters: An Application to Cointegration and Unit Roots

2016 ◽  
Vol 25 (2) ◽  
Author(s):  
Robert M. Kunst
Keyword(s):  
Time Series ◽  
Monte Carlo Simulation ◽  
Time Series Data ◽  
Decision Rules ◽  
Unit Roots ◽  
Series Data ◽  
Bivariate Model ◽  
Quarterly Data ◽  
Testing Problem ◽  
Optimum Decision

The problem of detecting unit roots in time series data is treated as a problem of multiple decisions instead of a testing problem, as is otherwise common in the econometric and statistical literature. The multiple decision design is based on a distinction between continuous primary and discrete secondary parameters. Four examples for such multiple decision designs are considered: first- and second-order integrated univariate processes; cointegration in a bivariate model; seasonal integration for semester data; seasonalintegration for quarterly data. In all cases, restricted optimum decision rules are established based on Monte Carlo simulation.

Six GPA Decision Rules and Adverse Impact in Initial Screening

2004 ◽  
Author(s):  
Kevin D. Carlson ◽  
Mary L. Connerley ◽  
Arlise P. McKinney ◽  
Ross L. Mecham
Keyword(s):  
Decision Rules ◽  
Adverse Impact ◽  
Initial Screening
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