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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Nested analysis of variance with autocorrelated errors.

S G Pantula, K H Pollock

|December 1, 1985

Summary

This summary is machine-generated.

This study explores analyzing repeated time measurements in experiments. An autoregressive time series model effectively handles time-induced correlations, offering a practical alternative to traditional split-plot analysis of variance.

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Area of Science:

Statistics
Experimental Design
Time Series Analysis

Background:

Analyzing data with repeated time measurements presents challenges.
Traditional split-plot analysis of variance may not fully capture temporal dependencies.

Purpose of the Study:

To propose and evaluate an autoregressive time series modeling approach for analyzing data from randomized experimental designs with successive time measurements.
To provide a practical alternative to split-plot analysis of variance for such data.

Main Methods:

The study applies an autoregressive time series modeling approach.
This method explicitly accounts for time-induced correlations within experimental units.
Estimation and hypothesis testing procedures are developed within this framework.

Main Results:

The autoregressive time series model is shown to be a practical and effective method for analyzing data with repeated time measurements.
The procedure is illustrated with two real-world examples, demonstrating its applicability.
The approach offers advantages in handling time-induced correlations.

Conclusions:

Autoregressive time series modeling provides a robust framework for analyzing complex experimental designs with longitudinal data.
This methodology offers a valuable alternative for researchers dealing with time-dependent observations.