Selection of the reference priors for a balanced random effects model

Test ◽  
1995 ◽  
Vol 4 (1) ◽  
pp. 1-17 ◽  
Author(s):  
K. Ye
Keyword(s):  
Random Effects ◽  
Random Effects Model ◽  
Reference Priors ◽  
Selection Of

The Impact of Using Incorrect Weights With the Multiple Membership Random Effects Model

Methodology ◽  
2014 ◽  
Vol 10 (1) ◽  
pp. 31-42 ◽  
Author(s):  
Lindsey J. Wolff Smith ◽  
S. Natasha Beretvas
Keyword(s):  
Random Effects ◽  
Data Structures ◽  
Simulation Study ◽  
Future Research ◽  
Small Scale ◽  
Random Effects Model ◽  
Real Dataset ◽  
Multiple Membership ◽  
The Impact ◽  
Selection Of

The multiple membership random effects model (MMREM) is used to appropriately model multiple membership data structures. Use of the MMREM requires selection of weights reflecting the hypothesized contribution of each level two unit (e.g., school) and their descriptors to the level one outcome. This study assessed the impact on MMREM parameter and residual estimates of the choice of weight pattern used. Parameter and residual estimates resulting from use of different weight patterns were compared using a real dataset and a small-scale simulation study. Under the conditions examined here, results indicated that choice of weight pattern did not greatly impact relative parameter bias nor level two residuals’ ranks. Limitations and directions for future research are discussed.

scholarly journals Determinants of Corporate Dividend Policy in Polish Companies Listed on WSE

e-Finanse ◽  
2019 ◽  
Vol 15 (3) ◽  
pp. 47-55
Author(s):  
Magdalena Gostkowska-Drzewicka ◽  
Ewa Majerowska
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Stock Exchange ◽  
Structural Parameters ◽  
Random Effects Model ◽  
Profit Distribution ◽  
Dividend Payments ◽  
Warsaw Stock Exchange ◽  
History Of ◽  
Selection Of

AbstractThe purpose of this paper is to identify the factors influencing the level of dividend payments in the companies listed on the Warsaw Stock Exchange in 1998-2017 as well as to provide empirical evidence for their significance, using a panel data approach. The object of research comprised the companies listed on WSE, as of February 01, 2019. The subject of the analysis are the dividends paid by the companies and the factors potentially influencing the decisions regarding profit distribution. The models estimated for the panel data, based on the theory, allowed selection of the best model, which is the random-effects model. Moreover, these models allowed identification of the factors determining the changes in the level of dividend per share. The best model was the random-effects model. This model allowed identification of the factors impacting the changes in the level of dividend per share, that is, the value of the company’s total assets and the history of the company’s operation on the stock exchange market. All structural parameters (except the intercept) were positive. It means that growth of each of these variables causes an increase in the dividend per share.

scholarly journals Updates to the Regional Seismic Travel Time (RSTT) Model: 2. Path-dependent Travel-time Uncertainty

2021 ◽  
Vol 178 (2) ◽  
pp. 313-339
Author(s):  
Michael L. Begnaud ◽  
Dale N. Anderson ◽  
Stephen C. Myers ◽  
Brian Young ◽  
James R. Hipp ◽  
...  
Keyword(s):  
Travel Time ◽  
Random Effects ◽  
Mean Squared Error ◽  
Random Effects Model ◽  
Location Error ◽  
Crust And Upper Mantle ◽  
Time Uncertainty ◽  
Uncertainty Estimates ◽  
Event Location ◽  
Travel Time Uncertainty

AbstractThe regional seismic travel time (RSTT) model and software were developed to improve travel-time prediction accuracy by accounting for three-dimensional crust and upper mantle structure. Travel-time uncertainty estimates are used in the process of associating seismic phases to events and to accurately calculate location uncertainty bounds (i.e. event location error ellipses). We improve on the current distance-dependent uncertainty parameterization for RSTT using a random effects model to estimate slowness (inverse velocity) uncertainty as a mean squared error for each model parameter. The random effects model separates the error between observed slowness and model predicted slowness into bias and random components. The path-specific travel-time uncertainty is calculated by integrating these mean squared errors along a seismic-phase ray path. We demonstrate that event location error ellipses computed for a 90% coverage ellipse metric (used by the Comprehensive Nuclear-Test-Ban Treaty Organization International Data Centre (IDC)), and using the path-specific travel-time uncertainty approach, are more representative (median 82.5% ellipse percentage) of true location error than error ellipses computed using distance-dependent travel-time uncertainties (median 70.1%). We also demonstrate measurable improvement in location uncertainties using the RSTT method compared to the current station correction approach used at the IDC (median 74.3% coverage ellipse).

Skin Color Matters in the Latinx Community: A Call for Action in Research, Training, and Practice

2021 ◽  
pp. 073998632199591
Author(s):  
Milton A. Fuentes ◽  
Jazmin A. Reyes-Portillo ◽  
Petty Tineo ◽  
Kenny Gonzalez ◽  
Mamona Butt
Keyword(s):  
Cultural Diversity ◽  
Random Effects ◽  
Undergraduate Students ◽  
Skin Color ◽  
Clinical Training ◽  
Research Training ◽  
Random Effects Model ◽  
Concerted Effort ◽  
Training And Research

While skin color is relevant and important in the Latinx community, as it is associated with colorism, little is known about how often it is measured or the best way to measure it. This article presents results from two studies examining these key concerns in three prominent journals, where Latinx research is typically published (i.e., the Hispanic Journal of Behavioral Sciences, the Journal of Latinx Psychology, and Cultural Diversity and Ethnic Minority Psychology). Study one examined whether skin color was measured as a variable, and if so, what measures and methodologies were used. A review of articles ( n = 1,137) showed few studies measured skin color in these three journals, with studies that did so relying on various approaches. Study two aimed to assess the reliability of a widely used skin color measure, the Massey-Martin scale, also known as the New Immigrant Survey (NIS) Skin Scale. Using a sample of 169 undergraduate students, self-ratings, coder ratings, and in vivo ratings were obtained and compared. One-way random effects model analyses indicated excellent reliability with minimal variability across the various ratings. Our findings suggest a critical need to engage in a more concerted effort to assess and discuss the relevance and importance of skin color within the Latinx community. The authors offer some suggestions on how to facilitate these efforts in clinical, training, and research arenas.

scholarly journals Gunshot Wounds to the Lumbosacral Spine: Systematic Review and Meta-Analysis

2021 ◽  
pp. 219256822110308
Author(s):  
Andrew Platt ◽  
Mostafa H. El Dafrawy ◽  
Michael J. Lee ◽  
Martin H. Herman ◽  
Edwin Ramos
Keyword(s):  
Systematic Review ◽  
Random Effects ◽  
Surgical Intervention ◽  
Meta Analysis ◽  
Gunshot Wounds ◽  
Operative Management ◽  
Operative Intervention ◽  
Lumbosacral Spine ◽  
Random Effects Model ◽  
Significant Difference

Study Design: Systematic review and meta-analysis. Objectives: Indications for surgical decompression of gunshot wounds to the lumbosacral spine are controversial and based on limited data. Methods: A systematic review of literature was conducted to identify studies that directly compare neurologic outcomes following operative and non-operative management of gunshot wounds to the lumbosacral spine. Studies were evaluated for degree of neurologic improvement, complications, and antibiotic usage. An odds ratio and 95% confidence interval were calculated for dichotomous outcomes which were then pooled by random-effects model meta-analysis. Results: Five studies were included that met inclusion criteria. The total rate of neurologic improvement was 72.3% following surgical intervention and 61.7% following non-operative intervention. A random-effects model meta-analysis was carried out which failed to show a statistically significant difference in the rate of neurologic improvement between surgical and non-operative intervention (OR 1.07; 95% CI 0.45, 2.53; P = 0.88). In civilian only studies, a random-effects model meta-analysis failed to show a statistically significant difference in the rate of neurologic improvement between surgical and non-operative intervention (OR 0.75; 95% CI 0.21, 2.72; P = 0.66). Meta-analysis further failed to show a statistically significant difference in the rate of neurologic improvement between patients with either complete (OR 4.13; 95% CI 0.55, 30.80; P = 0.17) or incomplete (OR 0.38; 95% CI 0.10, 1.52; P = 0.17) neurologic injuries who underwent surgical and non-operative intervention. There were no significant differences in the number of infections and other complications between patients who underwent surgical and non-operative intervention. Conclusions: There were no statistically significant differences in the rate of neurologic improvement between those who underwent surgical or non-operative intervention. Further research is necessary to determine if surgical intervention for gunshot wounds to the lumbosacral spine, including in the case of retained bullet within the spinal canal, is efficacious.

scholarly journals Gradient boosting for linear mixed models

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Colin Griesbach ◽  
Benjamin Säfken ◽  
Elisabeth Waldmann
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Selection Procedure ◽  
Classification Theory ◽  
Gradient Boosting ◽  
Random Structure ◽  
Boosting Algorithm ◽  
The One ◽  
Biased Estimates ◽  
Selection Of

Abstract Gradient boosting from the field of statistical learning is widely known as a powerful framework for estimation and selection of predictor effects in various regression models by adapting concepts from classification theory. Current boosting approaches also offer methods accounting for random effects and thus enable prediction of mixed models for longitudinal and clustered data. However, these approaches include several flaws resulting in unbalanced effect selection with falsely induced shrinkage and a low convergence rate on the one hand and biased estimates of the random effects on the other hand. We therefore propose a new boosting algorithm which explicitly accounts for the random structure by excluding it from the selection procedure, properly correcting the random effects estimates and in addition providing likelihood-based estimation of the random effects variance structure. The new algorithm offers an organic and unbiased fitting approach, which is shown via simulations and data examples.

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