scholarly journals On Selecting Spatial-Temporal Autologistic Regression Models for Binary Lattice Data

2012 ◽  
Vol 03 (06) ◽  
Author(s):  
Yanbing Zheng
Web Ecology ◽  
2008 ◽  
Vol 8 (1) ◽  
pp. 22-29 ◽  
Author(s):  
G. Carl ◽  
C. F. Dormann ◽  
I. Kühn

Abstract. Species distributional data based on lattice data often display spatial autocorrelation. In such cases, the assumption of independently and identically distributed errors can be violated in standard regression models. Based on a recently published review on methods to account for spatial autocorrelation, we describe here a new statistical approach which relies on the theory of wavelets. It provides a powerful tool for removing spatial autocorrelation without any prior knowledge of the underlying correlation structure. Our wavelet-revised model (WRM) is applied to artificial datasets of species’ distributions, for both presence/absence (binary response) and species abundance data (Poisson or normally distributed response). Making use of these published data enables us to compare WRM to other recently tested models and to recommend it as an attractive option for effective and computationally efficient autocorrelation removal.


2013 ◽  
Vol 18 (3) ◽  
pp. 429-449 ◽  
Author(s):  
Rao Fu ◽  
Andrew L. Thurman ◽  
Tingjin Chu ◽  
Michelle M. Steen-Adams ◽  
Jun Zhu

1975 ◽  
Vol 12 (04) ◽  
pp. 702-712 ◽  
Author(s):  
David J. Strauss

The probability density of binary variables on a lattice with the nearest-neighbor condition is given by the Gibbs Random Field formula. This paper examines some consequences of the result. Approximate formulae for the maximum likelihood estimator of the persistence parameter are derived and discussed, with an example. A comparison is made between two test statistics for independence.


2017 ◽  
Vol 22 (3) ◽  
pp. 413-419
Author(s):  
Rao Fu ◽  
Andrew L. Thurman ◽  
Tingjin Chu ◽  
Michelle M. Steen-adams ◽  
Jun Zhu

Viruses ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 550
Author(s):  
Christine L. Casey ◽  
Stephen L. Rathbun ◽  
David E. Stallknecht ◽  
Mark G. Ruder

Hemorrhagic disease (HD) is considered one of the most significant infectious diseases of white-tailed deer in North America. Investigations into environmental conditions associated with outbreaks suggest drought conditions are strongly correlated with outbreaks in some regions of the United States. However, during 2017, an HD outbreak occurred in the Eastern United States which appeared to be associated with a specific physiographic region, the Appalachian Plateau, and not drought conditions. The objective of this study was to determine if reported HD in white-tailed deer in 2017 was correlated with physiographic region. There were 456 reports of HD from 1605 counties across 26 states and 12 physiographic regions. Of the 93 HD reports confirmed by virus isolation, 76.3% (71/93) were identified as EHDV-2 and 66.2% (47/71) were from the Appalachian Plateau. A report of HD was 4.4 times more likely to occur in the Appalachian Plateau than not in 2017. Autologistic regression models suggested a statistically significant spatial dependence. The underlying factors explaining this correlation are unknown, but may be related to a variety of host, vector, or environmental factors. This unique outbreak and its implications for HD epidemiology highlight the importance for increased surveillance and reporting efforts in the future.


1975 ◽  
Vol 12 (4) ◽  
pp. 702-712 ◽  
Author(s):  
David J. Strauss

The probability density of binary variables on a lattice with the nearest-neighbor condition is given by the Gibbs Random Field formula. This paper examines some consequences of the result. Approximate formulae for the maximum likelihood estimator of the persistence parameter are derived and discussed, with an example. A comparison is made between two test statistics for independence.


1975 ◽  
Vol 7 (3) ◽  
pp. 452-452
Author(s):  
Peter Clifford

The practical problems involved in the maximum likelihood estimation of the parameters in a binary Markov field on a lattice are investigated. Methods using whole and conditional data are compared.


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