How Many Classes in the Pearson Chi-Square Test?

Dahiya, Ram C.; Gurland, John

doi:10.2307/2284803

Cited by 33 publications

(20 citation statements)

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“…However Williams (1950) shows that for n > 200, the value of k given by the Mann-Wald formula can be halved without significant loss of power. The more recent studies of Hamdan (1963), Dahiya and Gurland (1973), Gvanceladze and Chibisov (1979), Best and Rayner (1981), and Quine and Robinson (1985) indicate that the Mann-Wald formula may result in a choice of k that is too large, and reduces the power of the test against specific alternatives.…”

Section: Choosing the Cell Boundariesmentioning

confidence: 99%

Goodness-of-Fit Statistics for Discrete Multivariate Data

Read¹,

Cressie²

1988

Springer Series in Statistics

774

609

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Section: Choosing the Cell Boundariesmentioning

confidence: 99%

Goodness-of-Fit Statistics for Discrete Multivariate Data

Read¹,

Cressie²

1988

Springer Series in Statistics

774

609

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“…In this subsection, the correctness of the distributions of the identification and detection statistics, formulated as Eqs. (33), (39), (47) and (48), are verified.…”

Section: A Validation Of the Theoretical Analysismentioning

confidence: 63%

“…1) The Distribution of Identification Statistics: The correctness of the distributions of identification statistics D KL , formulated as Eqs. (33) and (39), can justify the distribution of the eigenvalues of matrix Φ R shown in Eq. (26).…”

Section: A Validation Of the Theoretical Analysismentioning

confidence: 75%

Short-Time Velocity Identification and Coherent-Like Detection of Ultrahigh Speed Targets

Wang¹,

Zhou²,

Li³

et al. 2018

IEEE Trans. Signal Process.

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“…Exceptions occur when testing for normality as shown in [13] and when the alternative has light tails compared to the hypothesized distribution [7, 12]. In these cases, small k is optimal for all values of n .…”

Section: Bin Selection For Xn2mentioning

confidence: 99%

The choice of the number of bins for the statistic

White

Bonetti

Pagano

2009

Computational Statistics & Data Analysis

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Methods to monitor spatial patterns of disease in populations are of interest in public health practice. The M statistic uses interpoint distances between cases to detect abnormalities in the spatial patterns of diseases. This statistic compares the observed distribution of interpoint distances with that which is expected when no unusual spatial patterns exist. We show the relationship of M to Pearson's Chi Square statistic, . Both statistics require the discretization of continuous data into bins and then are formed by creating a quadratic form, scaled by an appropriate variance covariance matrix. We seek to choose the number and type of these bins for the M statistic so as to maximize the power to detect spatial anomalies. By showing the relationship between M to , we argue for the extension of the theory that has been developed for the selection of the number and type of bins for to M. We further show that spatial data provides a unique insight into the problem through examples with simulated data and spatial data from a health care provider. In the spatial setting, these indicate that the optimal number of bins depends on the size of the cluster. For large clusters, a smaller number of bins appears to be preferrable, however for small clusters having many bins increases the power. Further, results indicate that the number of bins does not appear to vary with m, the number of spatial locations. We discuss the implications of this result for further work.

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How Many Classes in the Pearson Chi-Square Test?

Cited by 33 publications

References 0 publications

Goodness-of-Fit Statistics for Discrete Multivariate Data

Goodness-of-Fit Statistics for Discrete Multivariate Data

Short-Time Velocity Identification and Coherent-Like Detection of Ultrahigh Speed Targets

The choice of the number of bins for the statistic

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