A Simple Test for Uniformity

Dijkstra, Jan B.; Rietjens, Tjeu J. M.; Steutel, F.W.

doi:10.1111/j.1467-9574.1984.tb01095.x

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…Several tests exist for this purpose, with different uses depending on the alternative hypotheses (e.g. [3,29,30,31,32,33]). Some of the most useful are tests based on second and higher order properties [34,35,36,37].…”

Section: Model Evaluationmentioning

confidence: 99%

Introduction to Point Processes

Schoenberg

2011

Wiley Encyclopedia of Operations Research and Management Science

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Point process models are useful for describing phenomena occurring at random locations and/or times. Following a review of basic concepts, some important models are surveyed including Poisson processes, renewal processes, Hawkes processes, and Markovian point processes. Techniques for estimation, simulation and residual analysis for point processes are also briefly discussed.A point process is a random collection of points falling in some space. In most applications, each point represents the time and/or location of an event, such as a lightning strike or earthquake. When modeling purely temporal data, the space in which the points fall is simply a portion of the real line.

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Section: Model Evaluationmentioning

confidence: 99%

Introduction to Point Processes

Schoenberg

2011

Wiley Encyclopedia of Operations Research and Management Science

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“…In a non-regression setting an analogue of our type of test statistics is the goodness-of-fit test statistic in Dijkstra, Rietjens and Steutel (1984). In case S is compact, these authors propose to reject uniformity on S when P n = II).ax J.tj,n becomes too large, where the l<J<mn partition is taken to be such that under the null hypothesis the J.tj are all equal.…”

Section: Fj(y)mentioning

confidence: 99%

Maximal type test statistics based on conditional processes

Beirlant

Einmahl

1996

Journal of Statistical Planning and Inference

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.4 b8tract: A general methodology is presented for non-parametric testing of independence, location and dispersion in multiple regression. The proposed testing procedures are based on the concepts of conditional distribution function, conditional quantile, and conditional shortest t-fraction. Techniques involved come from empirical process and extreme-value theory. The asymptotic distributions are standard Gumbel.

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Section: Model Evaluationmentioning

confidence: 99%

Introduction to point processes

Møller¹,

Waagepetersen²

2003

Statistical Inference and Simulation for Spatial Point Processes

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A point process is a random collection of points falling in some space. In most applications, each point represents the time and/or location of an event, such as a lightning strike or earthquake. When modeling purely temporal data, the space in which the points fall is simply a portion of the real line. FORMAL DEFINITIONSThere are several ways of characterizing a point process. The usual approach is to define a point process N as a random measure on a complete separable metric space S taking values in the non-negative integers Z + (or infinity). In this framework the measure N(A) represents the number of points falling in the subset A of S. Attention is typically restricted to the case where N may contains only finitely many points on any bounded subset of S.The random measure definition above is perhaps not the most intuitive means of characterizing a point process, and several alternatives exist. Consider the case of a temporal point process, e.g. the times of events occurring between time 0 and time T.

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A Simple Test for Uniformity

Cited by 11 publications

References 7 publications

Introduction to Point Processes

Introduction to Point Processes

Maximal type test statistics based on conditional processes

Introduction to point processes

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