Language typology and statistics

Mapping Intimacies ◽

10.31219/osf.io/emafd ◽

2021 ◽

Author(s):

Emil O. W. Kirkegaard

Keyword(s):

Numerical Methods ◽

Spatial Autocorrelation ◽

Statistical Methods ◽

Language Typology ◽

Automated Methods

Statistical methods are presented to a linguistics audience. Statistical methods are then applied to the large WALS dataset to show that automated methods can identify patterns among language features. These results are shown to be more extreme than one would expect based on chance variation. Furthermore, it is shown numerically that language features exhibit spatial autocorrelation which needs to be taken into account when using numerical methods.

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Alternative methods for dealing by nonnormality and heteroscedasticity in paleontological data

Journal of Paleontology ◽

10.1017/s0022336000020989 ◽

1992 ◽

Vol 66 (6) ◽

pp. 857-867 ◽

Cited By ~ 6

Author(s):

Steven J. Hageman

Keyword(s):

Numerical Methods ◽

Statistical Methods ◽

Statistical Tests ◽

Practical Method ◽

Original Data ◽

Nonparametric Tests ◽

Alternative Methods ◽

Potential Utility ◽

Paleontological Data ◽

Potential Problems

Although numerical methods are highly useful in paleontological studies, potential problems arise with application of parametric statistical methods to paleontological data. Most common statistical tests assume data are normally distributed and that multiple populations have equal variances (homoscedasticity). Paleontological data frequently do not satisfy these assumptions, thereby affecting results of tests and potentially misleading scientific interpretations. Nonparametric tests should be used when assumptions of parametric tests are violated. Normal scores tests, which utilize expected normal deviates (rankits) substituted for original data, are the most powerful nonparametric tests. Despite their potential utility, normal scores tests have received little attention, primarily because of difficulties encountered with rankit conversion.Recent advances in microcomputer technology provide viable methods for rankit conversion, thus making normal scores tests accessible for routine application. Normal scores tests provide a practical method of dealing with nonnormality and heteroscedasticity common in paleontological data.

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Isolating Spatial Autocorrelation, Aggregation Bias, and Distributional Violations in Ecological Inference: Comment on Anselin and Cho

Political Analysis ◽

10.1093/pan/10.3.298 ◽

2002 ◽

Vol 10 (3) ◽

pp. 298-300 ◽

Cited By ~ 3

Author(s):

Gary King

Keyword(s):

Spatial Autocorrelation ◽

Statistical Methods ◽

Aggregation Bias ◽

Ecological Inference

Few better ways of checking and improving statistical methods exist than having other researchers go over your results, and so I especially appreciate the efforts in Anselin and Cho (2002), hereinafter AC. In this note, I make two main points.

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Laws of Geography

Oxford Research Encyclopedia of Criminology and Criminal Justice ◽

10.1093/acrefore/9780190264079.013.325 ◽

2018 ◽

Cited By ~ 2

Author(s):

Michael Leitner ◽

Philip Glasner ◽

Ourania Kounadi

Keyword(s):

Information System ◽

Geographic Information System ◽

Spatial Autocorrelation ◽

Statistical Methods ◽

Spatial Interpolation ◽

Main Part ◽

Crime Analysis ◽

Geospatial Technology ◽

Cold Spots ◽

Global And Local

The most prominent law in geography is Tobler’s first law (TFL) of geography, which states that “everything is related to everything else, but near things are more related than distant things.” No other law in geography has received more attention than TFL. It is important because many spatial statistical methods have been developed since its publication and, especially since the advent of geographic information system (GIS) and geospatial technology, have been conceptually based on it. These methods include global and local indicators of spatial autocorrelation (SA), spatial and spatial-temporal hotspots and cold spots, and spatial interpolation. All of these are highly relevant to spatial crime analysis, modeling, and mapping and will be discussed in the main part of this text.

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Statistics and parallaxes

International Astronomical Union Colloquium ◽

10.1017/s0252921100073875 ◽

1978 ◽

Vol 48 ◽

pp. 7-29

Author(s):

T. E. Lutz

Keyword(s):

Experimental Design ◽

Statistical Methods ◽

Review Paper ◽

Systematic Errors ◽

Past Experience ◽

Random Errors ◽

Trigonometric Parallaxes ◽

Systematic And Random Errors

This review paper deals with the use of statistical methods to evaluate systematic and random errors associated with trigonometric parallaxes. First, systematic errors which arise when using trigonometric parallaxes to calibrate luminosity systems are discussed. Next, determination of the external errors of parallax measurement are reviewed. Observatory corrections are discussed. Schilt’s point, that as the causes of these systematic differences between observatories are not known the computed corrections can not be applied appropriately, is emphasized. However, modern parallax work is sufficiently accurate that it is necessary to determine observatory corrections if full use is to be made of the potential precision of the data. To this end, it is suggested that a prior experimental design is required. Past experience has shown that accidental overlap of observing programs will not suffice to determine observatory corrections which are meaningful.

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