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Robert E. Machol

dblp:178/6298 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1987
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Information theory · 50% Mathematical optimization · 50%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Mathematical optimization › statistical estimation › interval estimation
confidence interval
0.011987
Confidence intervals based on one or more observations · IEEE Trans. Inf. Theory 1987
Information theory
statistical inference
0.011987
Confidence intervals based on one or more observations · IEEE Trans. Inf. Theory 1987

Methods — techniques the papers use, named apart from their topics

uniform distribution · 0.0student's t-distribution · 0.0cauchy distribution · 0.0
YearPublicationVenuePosition
1987 Confidence intervals based on one or more observations
abstract
On the basis ofn\geq 2observations, confidence limits of the formX̄ \pm tS/ \sqrt{n}are constructed for the location (e.g., the median) of any distribution of known form with unknown location and dispersion (scale), whereX̄andSare the sample mean and "unbiased" standard deviation. Particular attention is given to the values oftneeded for the Cauchy and uniform distributions. The lattertsuffices for any (unknown) symmetric unimodal distribution ift \geq n - 1. A table compares these values oftforn=2,3,4, and5with those for the normal case, which are derived here very simply and are identical with those found by "Student." We are also able to include the case of a single observation(n=1), where confidence intervals of various forms are made just wide enough for the least favorable dispersion. They, therefore, include the true location with at least but, in general, not exactly the desired probability; these intervals involve a predetermined value that plays a role reminiscent of but quite different from that of the prior distribution that would enter into a Bayesian analysis. In addition, upper confidence limits for the dispersion are constructed forn \geq 1.
Nelson M. Blachman, Robert E. Machol
IEEE Trans. Inf. Theory2