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Journal ArticleDOI

The use of time-series models and methods in the analysis of agricultural field trials

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TLDR
In this paper, it is suggested that the data analytic tools of time series analysis should be more widely used, and many of the problems of two-dimensional modelling can be overcome by using separable processes.
Abstract
Although many spatial methods for analysing field trials have been proposed, most are for a one-dimensional layout, and most prescribe a single model for all data. In this paper it is suggested that the data analytic tools of time series analysis should be more widely used. Many of the problems of two-dimensional modelling can be overcome by using separable processes. This subclass of lattice processes can often be reasonably used, and has several advantages, including rapid fitting and simple extensions of many techniques developed and successfully used in time series. Some examples are given.

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Citations
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Journal ArticleDOI

Bayesian Computation and Stochastic Systems

TL;DR: Basic methodology of MCMC is presented, emphasizing the Bayesian paradigm, conditional probability and the intimate relationship with Markov random fields in spatial statistics, and particular emphasis on the calculation of posterior probabilities.
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Average information REML: An efficient algorithm for variance parameter estimation in linear mixed models

TL;DR: In this article, a strategy of using an average information matrix is shown to be computationally convenient and efficient for estimating variance components by restricted maximum likelihood (REML) in the mixed linear model.
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On conditional and intrinsic autoregressions

TL;DR: In this article, the authors discuss standard and intrinsic autoregressions and describe how the problems that arise can be alleviated using Dempster's (1972) algorithm or an appropriate modification.
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Accounting for natural and extraneous variation in the analysis of field experiments.

TL;DR: The authors identify three major components of spatial variation in plot errors from field experiments and extend the two-dimensional spatial procedures of Cullis and Gleeson (1991) to account for them.
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The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches

TL;DR: The most common mixed model approaches for series of variety trials are mixed model versions of the methods summarized by Kempton (1984) as mentioned in this paper, and a general formulation that encompasses all of these methods is described, then individual methods are considered in detail.
References
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Book

Time series analysis, forecasting and control

TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Journal ArticleDOI

Time Series Analysis Forecasting and Control

TL;DR: This revision of a classic, seminal, and authoritative book explores the building of stochastic models for time series and their use in important areas of application —forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.
Book

Spatial statistics

Book

The Statistical Analysis of Time Series

TL;DR: The Wiley Classics Library as discussed by the authors is a collection of books that have become recognized classics in their respective fields, including some of the most important works of the 20th century in mathematics.
Journal ArticleDOI

On stationary processes in the plane

Peter Whittle
- 03 Dec 1954 - 
TL;DR: The sampling theory of stationary processes in space is not completely analogous to that of stationary time series, due to the fact that the variate of a time series is influenced only by past values, while for a spatial process dependence extends in all directions as mentioned in this paper.