On the location parameter confidence intervals based on a random size sample from a partially known population

1996 ◽  
Vol 81 (1) ◽  
pp. 2421-2423
Author(s):  
L. B. Klebanov ◽  
J. A. Melamed
Keyword(s):  
Confidence Intervals ◽  
Location Parameter ◽  
Random Size

scholarly journals Constructing confidence intervals for QTL location.

Genetics ◽  
1994 ◽  
Vol 138 (4) ◽  
pp. 1301-1308 ◽  
Author(s):  
B Mangin ◽  
B Goffinet ◽  
A Rebaï
Keyword(s):  
Confidence Interval ◽  
Linear Model ◽  
Asymptotic Distribution ◽  
Confidence Intervals ◽  
Likelihood Ratio Test ◽  
Location Parameter ◽  
Nuisance Parameters ◽  
Ratio Test ◽  
Almost All ◽  
Map Location

Abstract We describe a method for constructing the confidence interval of the QTL location parameter. This method is developed in the local asymptotic framework, leading to a linear model at each position of the putative QTL. The idea is to construct a likelihood ratio test, using statistics whose asymptotic distribution does not depend on the nuisance parameters and in particular on the effect of the QTL. We show theoretical properties of the confidence interval built with this test, and compare it with the classical confidence interval using simulations. We show in particular, that our confidence interval has the correct probability of containing the true map location of the QTL, for almost all QTLs, whereas the classical confidence interval can be very biased for QTLs having small effect.

Fixed Precision Estimation of a Positive Location Parameter of a Negative Exponential Population

1988 ◽  
Vol 37 (1-2) ◽  
pp. 101-104 ◽  
Author(s):  
N. Mukhopadhyay
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Scale Parameter ◽  
Asymptotic Properties ◽  
Location Parameter ◽  
Precision Estimation ◽  
Negative Exponential ◽  
Exponential Population

Sequential confidence intervals for the positive location parameter of a negative exponential population are studied when the scale parameter is unknown. Since traditional fixed-width confidence intervals here do not lead to satisfactory solutions, we propose a different approach to construct “fixed-precision” confidence intervals for the location. Various asymptotic properties of our confidence interval procedure are discussed briefly.

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