在品种霜冻试验的双向布局中,在纳入协变量时对异方差进行精确测试

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY
Angelika A. Pilkington, Brenton R. Clarke, Dean A. Diepeveen
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引用次数: 0

摘要

双向布局在谷物产业研究中很常见,因为谷物产业研究通常存在一个或多个协变量。人们普遍认为,在估计固定效应参数时,还应检查可能存在的额外误差方差结构。本文以西澳大利亚霜冻试验的数据集为例,说明了在存在协变量的情况下对异方差进行精确检验的方法。尽管该检验的一般代数在过去的文献中已为人所知,但当存在协变量时,对双向检验的实施还涉及计算问题。在这种情况下,检验结果表明比行业标准具有更大的功率,而且由于其精确的规模,在这种情况下比使用基于近似渐近分布的限制性最大似然比检验(REMLRT)更为可取。本文所考虑的精确检验方法包括在实验设计中建立适当的对比。我们将使用与双向布局线性模型中的指标集相对应的观测数据的具体选择来说明这一点。此外,还提供了一种对检验进行补充的算法。然后对规模和功率进行比较。当存在不平衡数据,并且可能存在一个以上的协变量时,该检验具有自然的扩展性。结果可扩展到平衡不完全区组设计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exact testing for heteroscedasticity in a two-way layout in variety frost trials when incorporating a covariate
Two-way layouts are common in grain industry research where it is often the case that there are one or more covariates. It is widely recognised that when estimating fixed effect parameters, one should also examine for possible extra error variance structure. An exact test for heteroscedasticity, when there is a covariate, is illustrated for a data set from frost trials in Western Australia. While the general algebra for the test is known, albeit in past literature, there are computational aspects of implementing the test for the two way when there are covariates. In this scenario the test is shown to have greater power than the industry standard, and because of its exact size, is preferable to use of the restricted maximum likelihood ratio test (REMLRT) based on the approximate asymptotic distribution in this instance. Formulation of the exact test considered here involves creation of appropriate contrasts in the experimental design. This is illustrated using specific choices of observations corresponding to an index set in the linear model for the two-way layout. Also an algorithm supplied complements the test. Comparisons of size and power then ensue. The test has natural extensions when there are unbalanced data, and more than one covariate may be present. Results can be extended to Balanced Incomplete Block Designs.
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来源期刊
Australian & New Zealand Journal of Statistics
Australian & New Zealand Journal of Statistics 数学-统计学与概率论
CiteScore
1.30
自引率
9.10%
发文量
31
审稿时长
>12 weeks
期刊介绍: The Australian & New Zealand Journal of Statistics is an international journal managed jointly by the Statistical Society of Australia and the New Zealand Statistical Association. Its purpose is to report significant and novel contributions in statistics, ranging across articles on statistical theory, methodology, applications and computing. The journal has a particular focus on statistical techniques that can be readily applied to real-world problems, and on application papers with an Australasian emphasis. Outstanding articles submitted to the journal may be selected as Discussion Papers, to be read at a meeting of either the Statistical Society of Australia or the New Zealand Statistical Association. The main body of the journal is divided into three sections. The Theory and Methods Section publishes papers containing original contributions to the theory and methodology of statistics, econometrics and probability, and seeks papers motivated by a real problem and which demonstrate the proposed theory or methodology in that situation. There is a strong preference for papers motivated by, and illustrated with, real data. The Applications Section publishes papers demonstrating applications of statistical techniques to problems faced by users of statistics in the sciences, government and industry. A particular focus is the application of newly developed statistical methodology to real data and the demonstration of better use of established statistical methodology in an area of application. It seeks to aid teachers of statistics by placing statistical methods in context. The Statistical Computing Section publishes papers containing new algorithms, code snippets, or software descriptions (for open source software only) which enhance the field through the application of computing. Preference is given to papers featuring publically available code and/or data, and to those motivated by statistical methods for practical problems.
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