Distribution-free confidence intervals for quantiles and tolerance intervals in terms ofk-records

2009 ◽  
Vol 79 (10) ◽  
pp. 1219-1233 ◽  
Author(s):  
J. Ahmadi ◽  
N. Balakrishnan
Keyword(s):  
Confidence Intervals ◽  
Distribution Free ◽  
Tolerance Intervals

Tolerance Intervals for True Scores

1985 ◽  
Vol 10 (1) ◽  
pp. 1-17 ◽  
Author(s):  
David Jarjoura
Keyword(s):  
Confidence Intervals ◽  
Practical Implication ◽  
Educational Measurement ◽  
True Score ◽  
Tolerance Intervals ◽  
Observed Score ◽  
True Scores

Issues regarding tolerance and confidence intervals are discussed within the context of educational measurement and conceptual distinctions are drawn between these two types of intervals. Points are raised about the advantages of tolerance intervals when the focus is on a particular observed score rather than a particular examinee. Because tolerance intervals depend on strong true score models, a practical implication of the study is that true score tolerance intervals are fairly insensitive to differences in assumptions among the five models studied.

Distribution-Free Confidence Intervals

1972 ◽  
Vol 26 (1) ◽  
pp. 39 ◽  
Author(s):  
Gottfried E. Noether
Keyword(s):  
Confidence Intervals ◽  
Distribution Free

Distribution-free confidence intervals for the standardized median

Stat ◽  
2013 ◽  
Vol 2 (1) ◽  
pp. 184-196 ◽  
Author(s):  
Robert G. Staudte
Keyword(s):  
Confidence Intervals ◽  
Distribution Free

Distribution-free confidence intervals for measurement of effective bandwidths

1999 ◽  
Vol 27 (3) ◽  
pp. 19
Author(s):  
Laszlo Gyorfi ◽  
Andras Racz ◽  
Ken Duffy ◽  
John T. Lewis ◽  
Raymond Russell ◽  
...  
Keyword(s):  
Confidence Intervals ◽  
Distribution Free ◽  
Effective Bandwidths

Simple Distribution-Free Confidence Intervals for a Difference in Location

Biometrics ◽  
1970 ◽  
Vol 26 (4) ◽  
pp. 871
Author(s):  
W. T. Federer ◽  
P. Van Der Laan
Keyword(s):  
Confidence Intervals ◽  
Distribution Free

scholarly journals Distribution-free confidence intervals for measurement of effective bandwidth

2000 ◽  
Vol 37 (1) ◽  
pp. 224-235 ◽  
Author(s):  
László Györfi ◽  
András Rácz ◽  
Ken Duffy ◽  
John T. Lewis ◽  
Fergal Toomey
Keyword(s):  
Confidence Intervals ◽  
Interval Estimation ◽  
Effective Bandwidth ◽  
Distribution Free ◽  
Peak Rate ◽  
Estimation Procedures ◽  
Hoeffding’S Inequality ◽  
Traffic Source

Hoeffding's inequality can be used in conjunction with the declared parameters of a traffic source, such as its peak rate, to obtain confidence intervals for measurements of the traffic's effective bandwidth. We describe a variety of interval-estimation procedures based on this idea, designed to provide differing degrees of robustness against non-stationarity. We also discuss how to compute confidence intervals for the effective bandwidth of an aggregate of traffic sources.

Sign in / Sign up

Export Citation Format

Share Document