Power studies of some tests for uniformity

Quesenberry, C. P.; Miller, Frank L.

doi:10.1080/00949657708810150

Cited by 82 publications

(36 citation statements)

References 31 publications

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“…As a result, nowadays there is strong evidence that, although the tests are omnibus, for moderate sample sizes only a few deviations from f 0 can be detected by these tests with substantial frequency. Simulation results confirming this observation can be found in Quesenberry and Miller (1977), Locke and Spurrier (1978), Miller and Quesenberry (1979), Eubank and LaRiccia (1992) and Kim (1992).…”

supporting

confidence: 78%

Data driven smooth tests for composite hypotheses

Inglot¹,

Kallenberg²,

Ledwina³

1997

Ann. Statist.

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The classical problem of testing goodness-of-fit of a parametric family is reconsidered. A new test for this problem is proposed and investigated. The new test statistic is a combination of the smooth test statistic and Schwarz's selection rule. More precisely, as the sample size increases, an increasing family of exponential models describing departures from the null model is introduced and Schwarz's selection rule is presented to select among them. Schwarz's rule provides the "right" dimension given by the data, while the smooth test in the "right" dimension finishes the job. Theoretical properties of the selection rules are derived under null and alternative hypotheses. They imply consistency of data driven smooth tests for composite hypotheses at essentially any alternative.

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supporting

confidence: 78%

Data driven smooth tests for composite hypotheses

Inglot¹,

Kallenberg²,

Ledwina³

1997

Ann. Statist.

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“…If "more complicated" is taken to mean "ω > 2", then the current study does address their key conclusion. Unlike the alternatives used here, the alternatives used in power studies by Quesenberry and Miller (1977), Miller and Quesenberry (1979) and most other authors, are indexed discretely rather than continuously. Also the range of alternatives accessed is not as broad as we are able to consider using our family.…”

Section: Size and Power Studymentioning

confidence: 99%

“…Quesenberry and Miller (1977) and Miller and Quesenberry (1979) both consider testing for uniformity, but neither considered the tests based on the components V r , r = 1, . .…”

Section: Size and Power Studymentioning

confidence: 99%

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Power of the Neyman smooth tests for the uniform distribution

Rayner

2001

Journal of Applied Mathematics and Decision Sciences

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Abstract. This paper compares and investigates the generalised Neyman smooth test, its components, and the classical chi-squared test with a variety of equiprobable classes. Each test is evaluated in terms of its power to reject a wavelike alternative to the uniform distribution, chosen to quantify the complexity of the alternative. Results indicate that if broadly focused tests (rather than strongly directional or weakly omnibus) are sought, then smooth tests of order about four, or the chi-squared test with between five and ten classes, will perform well.

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“…Quesenberry and Miller (1977) and Miller and Quesenberry (1979) have given power studies useful in selecting tests for simple uniformity. One good test for uniformity is the Neyman smooth test, which was recently set out in detail in Quesenberry and Quesenberry (1982 of this paper is to study this issue.…”

Section: Introduction Conditional Probability Integral Transformationmentioning

confidence: 99%

Agreement probabilities for some CPIT—neyman smooth tests

Quesenberry

Dietz

1983

Journal of Statistical Computation and Simulation

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The CPIT transformations do not, in general, give the same set of transformed values for different orderings of the observations in a sample. When these transformations are followed by a test of uniformity to give an overall goodness-of-fit test, it is possible to obtain different results for different orderings of a sample. We consider here the probability that two goodness-of-fit tests based on randomly selected permutations of the same sample and a Neyman smooth uniformity test will .~agree in their conclusions. We observe for the cases considered in a simulation study that the probability of agreement is generally large and tends to one with increasing n. KEY WORDS: Conditional probability integral transformations, Neyman smooth test, agreement probabilities, conjectured lower bound.

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Power studies of some tests for uniformity

Cited by 82 publications

References 31 publications

Data driven smooth tests for composite hypotheses

Data driven smooth tests for composite hypotheses

Power of the Neyman smooth tests for the uniform distribution

Agreement probabilities for some CPIT—neyman smooth tests

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