Small Sample Properties of Robust Analysis of Variance for Fixed and Random Effects Models.

1982 ◽  
Author(s):  
Ronald M. Schrader ◽  
Joseph W. McKean
Keyword(s):  
Random Effects ◽  
Analysis Of Variance ◽  
Small Sample ◽  
Robust Analysis ◽  
Random Effects Models ◽  
Fixed And Random Effects ◽  
Small Sample Properties

scholarly journals Random effects models for complex designs

2020 ◽  
Vol 29 (12) ◽  
pp. 3695-3706
Author(s):  
RG Jarrett ◽  
VT Farewell ◽  
AM Herzberg
Keyword(s):  
Random Effects ◽  
Analysis Of Variance ◽  
Two Phase ◽  
Full Account ◽  
Medical Practitioners ◽  
Random Effects Models ◽  
Multiple Measurements ◽  
Variance Table ◽  
Error Terms ◽  
Phase Design

Plaid designs are characterised by having one set of treatments applied to rows and another set of treatments applied to columns. In a 2003 publication, Farewell and Herzberg presented an analysis of variance structure for such designs. They presented an example of a study in which medical practitioners, trained in different ways, evaluated a series of videos of patients obtained under a variety of conditions. However, their analysis did not take full account of all error terms. In this paper, a more comprehensive analysis of this study is presented, informed by the recognition that the study can also be regarded as a two-phase design. The development of random effects models is outlined and the potential importance of block-treatment interactions is highlighted. The use of a variety of techniques is shown to lead to a better understanding of the study. Examination of the variance components involved in the expected mean squares is demonstrated to have particular value in identifying appropriate error terms for F-tests derived from an analysis of variance table. A package such as ASReml can also be used provided an appropriate error structure is specified. The methods presented can be applied to the design and analysis of other complex studies in which participants supply multiple measurements under a variety of conditions.

scholarly journals Significance of supply chain finance: Insights from Saudi Arabia

2021 ◽  
Vol 9 (3) ◽  
pp. 539-548 ◽  
Author(s):  
Abdul Rahman Shaik
Keyword(s):  
Saudi Arabia ◽  
Supply Chain ◽  
Financial Performance ◽  
Random Effects ◽  
Return On Assets ◽  
Random Effects Models ◽  
Performance Variables ◽  
Operating Profit ◽  
Supply Chain Finance ◽  
Fixed And Random Effects

The study examines the effect of the supply chain finance (SCF) on the corporate financial performance measured in terms of Return on Assets (ROA), Tobin's Q, and Gross Operating Profit (GOP) in the material sector of Saudi Arabia. The study selects a sample of 42 companies from the material sector listed on Tadawul starting in 2008 and ending 2019. A panel regression in terms of pooled OLS, fixed and random effects, and panel GMM is estimated to report the empirical results. The results report a negative and significant effect between the financial performance variables and supply chain finance, specifically with ROA with pooled OLS and fixed and random effects models. The results of panel GMM also show a negative and significant effect between all the financial performance variables and financing supply chain. The results are useful to academicians and the managers in the materials, inventory, and sales sections, and supply chain managers to integrate finance and SCM to achieve corporate benefits.

scholarly journals Non-Negativity of a Quadratic form with Applications to Panel Data Estimation, Forecasting and Optimization

Stats ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 185-202
Author(s):  
Bhimasankaram Pochiraju ◽  
Sridhar Seshadri ◽  
Dimitrios Thomakos ◽  
Konstantinos Nikolopoulos
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Symmetric Matrix ◽  
Panel Data Models ◽  
Random Effects Models ◽  
Panel Data Estimation ◽  
Quadratic Minimization ◽  
Fixed And Random Effects ◽  
Estimated Error ◽  
Data Estimation

For a symmetric matrix B, we determine the class of Q such that Q t BQ is non-negative definite and apply it to panel data estimation and forecasting: the Hausman test for testing the endogeneity of the random effects in panel data models. We show that the test can be performed if the estimated error variances in the fixed and random effects models satisfy a specific inequality. If it fails, we discuss the restrictions under which the test can be performed. We show that estimators satisfying the inequality exist. Furthermore, we discuss an application to a constrained quadratic minimization problem with an indefinite objective function.

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