Statistical Inference Theories, Multiple Use of the Same Data and Past-Realized Data

2015 ◽  
Author(s):  
Benjamin Holcblat ◽  
Steffen Gronneberg
Keyword(s):  
Statistical Inference ◽  
Multiple Use

scholarly journals On the Exact Two-Sided Tolerance Intervals for Univariate Normal Distribution and Linear Regression

2014 ◽  
Vol 43 (4) ◽  
pp. 279-292 ◽  
Author(s):  
Viktor Witkovsky
Keyword(s):  
Linear Regression ◽  
Statistical Inference ◽  
Theoretical Concept ◽  
Theoretical Background ◽  
Tolerance Interval ◽  
Multiple Use ◽  
Tolerance Intervals ◽  
Calibration Experiment ◽  
Statistical Tolerance ◽  
Tolerance Factors

Statistical tolerance intervals are another tool for making statistical inference on anunknown population. The tolerance interval is an interval estimator based on the resultsof a calibration experiment, which can be asserted with stated confidence level 1 ? ,for example 0.95, to contain at least a specified proportion 1 ? , for example 0.99, ofthe items in the population under consideration. Typically, the limits of the toleranceintervals functionally depend on the tolerance factors. In contrast to other statisticalintervals commonly used for statistical inference, the tolerance intervals are used relativelyrarely. One reason is that the theoretical concept and computational complexity of thetolerance intervals is significantly more difficult than that of the standard confidence andprediction intervals.In this paper we present a brief overview of the theoretical background and approachesfor computing the tolerance factors based on samples from one or several univariate normal(Gaussian) populations, as well as the tolerance factors for the non-simultaneousand simultaneous two-sided tolerance intervals for univariate linear regression. Such toleranceintervals are well motivated by their applicability in the multiple-use calibrationproblem and in construction of the calibration confidence intervals. For illustration, wepresent examples of computing selected tolerance factors by the implemented algorithmin MATLAB.

Statistical Inference for Measurement Models

1970 ◽  
Vol 15 (6) ◽  
pp. 402, 404-405
Author(s):  
ROBERT E. DEAR
Keyword(s):  
Statistical Inference ◽  
Measurement Models

Distribution characteristics on indoor radon concentration of multiple-use facilities in Gwangju area

2019 ◽  
Vol 18 (2) ◽  
pp. 177-184 ◽  
Author(s):  
Min-jin Kim ◽  
Sang-su An ◽  
Min-cheol Cho ◽  
Se-il Park ◽  
Jong-min Kim ◽  
...  
Keyword(s):  
Radon Concentration ◽  
Indoor Radon ◽  
Distribution Characteristics ◽  
Multiple Use ◽  
Indoor Radon Concentration
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