On selecting a subset which contains all populations better than a standard
Gupta, S.S.
On selecting a subset which contains all populations better than a standard - USA : Institute of Mathematical Statistics, 1957. - Printed
Open Access
A procedure is given for selecting a subset such that the probability that all the populations better than the standard are included in the subset is equal to or greater than a predetermined number
P
∗
. Section 3 deals with the problem of the location parameter for the normal distribution with known and unknown variance. Section 4 deals with the scale parameter problem for the normal distribution with known and unknown mean as well as the chi-square distribution. Section 5 deals with binomial distributions where the parameter of interest is the probability of failure on a single trial. In each of the above cases the case of known standard and unknown standard are treated separately. Tables are available for some problems; in other problems transformations are used such that the given tables are again appropriate.
Text in English
0090-5364
https://doi.org/10.1214/aoms/1177706721
Data analysis
Methods
Models
Population
Statistical analysis
On selecting a subset which contains all populations better than a standard - USA : Institute of Mathematical Statistics, 1957. - Printed
Open Access
A procedure is given for selecting a subset such that the probability that all the populations better than the standard are included in the subset is equal to or greater than a predetermined number
P
∗
. Section 3 deals with the problem of the location parameter for the normal distribution with known and unknown variance. Section 4 deals with the scale parameter problem for the normal distribution with known and unknown mean as well as the chi-square distribution. Section 5 deals with binomial distributions where the parameter of interest is the probability of failure on a single trial. In each of the above cases the case of known standard and unknown standard are treated separately. Tables are available for some problems; in other problems transformations are used such that the given tables are again appropriate.
Text in English
0090-5364
https://doi.org/10.1214/aoms/1177706721
Data analysis
Methods
Models
Population
Statistical analysis