Inference about the shape parameters of several inverse Gaussian distributions: testing equality and confidence interval for a common value

Metrika ◽  
2018 ◽  
Vol 82 (5) ◽  
pp. 529-545
Author(s):  
Mohammad Reza Kazemi ◽  
Ali Akbar Jafari
Keyword(s):  
Confidence Interval ◽  
Inverse Gaussian ◽  
Shape Parameters ◽  
Gaussian Distributions ◽  
Common Value

Optimum Tests for Comparison of two Inverse Guassian Shape Parameters

1988 ◽  
Vol 37 (3-4) ◽  
pp. 233-236
Author(s):  
A. Parsian ◽  
N. Sanjari Farsipour
Keyword(s):  
Nuisance Parameters ◽  
Inverse Gaussian ◽  
Shape Parameters ◽  
Test Procedures ◽  
Gaussian Distributions ◽  
Presence And Absence ◽  
Sample Case

In this article we consider the two sample case to eompare shape parameters of two Inverse Gaussian distributions. Optimum test procedures for both one­sides and two­sides cases are derived, in the presence and absence of the nuisance parameters.

Precision of sensitivity estimations in diagnostic test evaluations. Power functions for comparisons of sensitivities of two tests.

1985 ◽  
Vol 31 (4) ◽  
pp. 574-580 ◽  
Author(s):  
K Linnet
Keyword(s):  
Confidence Interval ◽  
Sample Size ◽  
Diagnostic Test ◽  
Diagnostic Tests ◽  
Sample Sizes ◽  
Power Functions ◽  
Gaussian Distributions ◽  
Sensitivity Estimate

Abstract The precision of estimates of the sensitivity of diagnostic tests is evaluated. "Sensitivity" is defined as the fraction of diseased subjects with test values exceeding the 0.975-fractile of the distribution of control values. An estimate of the sensitivity is subject to sample variation because of variation of both control observations and patient observations. If gaussian distributions are assumed, the 0.95-confidence interval for a sensitivity estimate is up to +/- 0.15 for a sample of 100 controls and 100 patients. For the same sample size, minimum differences of 0.08 to 0.32 of sensitivities of two tests are established as significant with a power of 0.90. For some published diagnostic test evaluations the median sample sizes for controls and patients were 63 and 33, respectively. I show that, to obtain a reasonable precision of sensitivity estimates and a reasonable power when two tests are being compared, the number of samples should in general be considerably larger.

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