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.

Distribution-free confidence intervals for measurement of effective bandwidth

2000 ◽  
Vol 37 (01) ◽  
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.

Interval Estimation for True Raw and Scale Scores Under the Binomial Error Model

2006 ◽  
Vol 31 (3) ◽  
pp. 261-281 ◽  
Author(s):  
Won-Chan Lee ◽  
Robert L. Brennan ◽  
Michael J. Kolen
Keyword(s):  
Confidence Intervals ◽  
Scale Score ◽  
Normal Approximation ◽  
Interval Estimation ◽  
Correct Score ◽  
True Number ◽  
Coverage Probabilities ◽  
Estimation Procedures ◽  
Scale Scores ◽  
Practical Guidelines

Assuming errors of measurement are distributed binomially, this article reviews various procedures for constructing an interval for an individual’s true number-correct score; presents two general interval estimation procedures for an individual’s true scale score (i.e., normal approximation and endpoints conversion methods); compares various interval estimation procedures through a computer simulation study; and provides some practical guidelines for use of the interval estimation procedures. To examine the effects of different types of scale scores, three nonlinearly transformed scale scores are employed. The conditional confidence intervals using conditional standard errors of measurement are recommended over the traditional confidence intervals using the overall standard error of measurement. For raw scores, the score confidence intervals, in general, tend to provide actual coverage probabilities that are closest to the nominal level. Results for scale score intervals seem to favor the endpoints conversion method using the true-score conversions over the normal approximation approach.

Asymptotic Distribution Free Interval Estimation

Methodology ◽  
2008 ◽  
Vol 4 (1) ◽  
pp. 4-9 ◽  
Author(s):  
Donna L. Coffman ◽  
Alberto Maydeu-Olivares ◽  
Jaume Arnau
Keyword(s):  
Confidence Intervals ◽  
Intraclass Correlation ◽  
Interval Estimation ◽  
Point Estimate ◽  
Distribution Free ◽  
Free Interval ◽  
Distributional Assumptions ◽  
Asymptotically Distribution Free ◽  
Coverage Rates ◽  
Better Than

Abstract. Confidence intervals for the intraclass correlation coefficient (ICC) have been proposed under the assumption of multivariate normality. We propose confidence intervals which do not require distributional assumptions. We performed a simulation study to assess the coverage rates of normal theory (NT) and asymptotically distribution free (ADF) intervals. We found that the ADF intervals performed better than the NT intervals when kurtosis was greater than 4. When violations of distributional assumptions were not too severe, both the intervals performed about the same. The point estimate of the ICC was robust to distributional violations. We provide R code for computing the ADF confidence intervals for the ICC.

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
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