On confidence limits associated with chow and shao's joint confidence region approach for assessment of bioequivalence

1997 ◽  
Vol 7 (1) ◽  
pp. 125-134 ◽  
Author(s):  
Hsing-Chu Hsu ◽  
Hai-Lin Lu
Keyword(s):  
Confidence Region ◽  
Confidence Limits ◽  
Joint Confidence Region ◽  
Region Approach

Some Considerations on Tool-Life Scatter and Its Implications

1975 ◽  
Vol 97 (3) ◽  
pp. 945-950 ◽  
Author(s):  
R. Levi ◽  
S. Rossetto
Keyword(s):  
Information Content ◽  
Tool Life ◽  
Test Data ◽  
Confidence Region ◽  
Life Test ◽  
Joint Confidence Region ◽  
Life Model ◽  
Personal Probability ◽  
Region Approach ◽  
Uncertainty Of Parameters

The effect of tool-life scatter on the uncertainty of parameters of a typical tool-life model is analyzed using the joint confidence region approach. Some apparent contradictions concerning tool-life test data are explained in terms of their information content in the light of personal probability concepts.

A Confidence Region Approach for Assessing Equivalence in Variability of Bioavailability

1996 ◽  
Vol 38 (4) ◽  
pp. 475-487 ◽  
Author(s):  
Keh-Wei Chen ◽  
Gang Li ◽  
Yanging Sun ◽  
Shein-Chung Chow
Keyword(s):  
Confidence Region ◽  
Region Approach

Joint Confidence Region for the Tuning Parameters of the PID Controller

2012 ◽  
Vol 45 (3) ◽  
pp. 727-732
Author(s):  
Nilton Silva ◽  
Heleno Bispo ◽  
Romildo Brito ◽  
João Manzi
Keyword(s):  
Pid Controller ◽  
Confidence Region ◽  
Tuning Parameters ◽  
Joint Confidence Region

scholarly journals Confidence Region Approach for Assessing Bioequivalence and Biosimilarity Accounting for Heterogeneity of Variability

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Jianghao Li ◽  
Shein-Chung Chow
Keyword(s):  
Drug Absorption ◽  
Generic Drugs ◽  
Confidence Region ◽  
Bioequivalence Study ◽  
Finite Sample ◽  
Drug Products ◽  
Test Product ◽  
Parameter Approach ◽  
Region Approach ◽  
Two Parameter

For approval of generic drugs, the FDA requires that evidence of bioequivalence in average bioequivalence in terms of drug absorption be provided through the conduct of a bioequivalence study. A test product is said to be average bioequivalent to a reference (innovative) product if the 90% confidence interval of the ratio of means (after log-transformation) is totally within (80%, 125%). This approach is considered a one-parameter approach, which does not account for possible heterogeneity of variability between drug products. In this paper, we study a two-parameter approach (i.e., confidence region approach) for assessing bioequivalence, which can also be applied to assessing biosimilarity of biosimilar products. The proposed confidence region approach is compared with the traditional one-parameter approach both theoretically and numerically (i.e., simulation study) for finite sample performance.

scholarly journals On Double Value at Risk

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 31 ◽  
Author(s):  
Wanbing Zhang ◽  
Sisi Zhang ◽  
Peibiao Zhao
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Confidence Region ◽  
Ratio Method ◽  
Two Dimensional ◽  
Income Risk ◽  
One Dimensional ◽  
Joint Confidence Region ◽  
The Empirical Analysis ◽  
Likelihood Ratio Method

Value at Risk (VaR) is used to illustrate the maximum potential loss under a given confidence level, and is just a single indicator to evaluate risk ignoring any information about income. The present paper will generalize one-dimensional VaR to two-dimensional VaR with income-risk double indicators. We first construct a double-VaR with ( μ , σ 2 ) (or ( μ , V a R 2 ) ) indicators, and deduce the joint confidence region of ( μ , σ 2 ) (or ( μ , V a R 2 ) ) by virtue of the two-dimensional likelihood ratio method. Finally, an example to cover the empirical analysis of two double-VaR models is stated.

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