scholarly journals Lower Confidence Bounds for the Probabilities of Correct Selection

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Radhey S. Singh ◽  
Narinder Kumar
Keyword(s):  
Scale Parameter ◽  
Real Life ◽  
Correct Selection ◽  
Probability Models ◽  
Confidence Bounds ◽  
Lower Confidence ◽  
Normal Probability ◽  
Location Parameters ◽  
Lower Confidence Bounds ◽  
General Scale

We extend the results of Gupta and Liang (1998), derived for location parameters, to obtain lower confidence bounds for the probability of correctly selecting thetbest populations(PCSt)simultaneously for allt=1,…,k−1for the general scale parameter models, wherekis the number of populations involved in the selection problem. The application of the results to the exponential and normal probability models is discussed. The implementation of the simultaneous lower confidence bounds forPCStis illustrated through real-life datasets.

Agreement among High School Diving Judges

1982 ◽  
Vol 55 (1) ◽  
pp. 3-7 ◽  
Author(s):  
Michael J. Stewart ◽  
William O. Blair
Keyword(s):  
High School ◽  
Confidence Intervals ◽  
Intraclass Correlation ◽  
Confidence Bounds ◽  
Point Estimates ◽  
Lower Confidence ◽  
Relative Consistency ◽  
Position Type ◽  
Lower Confidence Bounds ◽  
Actual Point

The analysis of aesthetic sports by a panel of judges has received some attention in past literature, but more studies have employed interclass correlations to arrive at judges' agreement or relative consistency. The purpose of this study was to determine raters' agreement and relative consistency of five male diving judges at the Kansas State Boys' Swimming and Diving Championships using intraclass correlation. Furthermore, one-sided confidence intervals were formed for analysis of sample variance. A total of 249 dives were performed and these dives were categorized into 16 position × type combinations for the analysis. Judges' variance was significant for 5 of the 16 type × position combinations. As expected divers' variance was significant for 14 of the 16 type × position combinations. Judges appeared to be somewhat consistent across dives but they were unable to agree upon the score of each dive. In other words the point estimates for consistency were generally greater than the point estimates for raters' agreement. Yet in neither case, consistency or raters' agreement, were lower confidence bounds impressively close to the actual point estimates.

Prescription and Surveillance of the Acetate-Free Biofiltration Sessions: The Bicarbonate Cycle

1995 ◽  
Vol 18 (11) ◽  
pp. 722-725 ◽  
Author(s):  
A. Pacitti ◽  
F. G. Casino ◽  
L. Pedrini ◽  
A. Santoro ◽  
M. Atti
Keyword(s):  
Large Population ◽  
Response Analysis ◽  
Confidence Bounds ◽  
Plasma Bicarbonate ◽  
Lower Confidence ◽  
Urea Kinetics ◽  
Lower Confidence Bounds ◽  
Cycle Covers ◽  
Computerized System ◽  
Sodium Handling

A computerized system, structured by 4 different models concerning urea depuration, and bicarbonate and sodium handling in acetate-free hemodiafiltration has been conceived for integrated use covering each step of the therapeutic cycle, from a) the prescription of the session to b) its delivery, up to c) the dose - response analysis: the system, now fully developed for the bicarbonate cycle, covers both working areas; the medical one, with a program implemented on a Personal Computer, called Skipper which deals with steps a) and c), and the nursing area, with a program built into the dialytic equipment software. The Skipper program supports the prescription step (a) testing the session schedule by bicarbonate, sodium and urea kinetics. The dialytic equipment, (step(b)) using a different program, on the basis of the scheduled parameters memorizes the end-session plasma bicarbonate level and reacts to any modifications of the parameters regarding blood flow and fluid reinfusion flow suggesting opposite changes in order to reach the scheduled results. Finally (step (c)), the Skipper system statistically evaluates the observed end session bicarbonate plasma level with an expected value with upper and lower confidence bounds obtained by a multiple regression analysis performed on a large population of patients.

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