The Generalized Linear Mixed Cluster-Weighted Model

In order to research dynamic network astringency reaching uniformity, this paper perfects the Vicsek model and puts forward improving dynamic network astringency efficiency by weighted model. We prove that the convergence rate of weighted model is faster than the classic Vicsek model and it can optimize dynamic network.

Download Full-text

Opportunity Costs in the Game of Best Choice

The Electronic Journal of Combinatorics ◽

10.37236/8322 ◽

2019 ◽

Vol 26 (1) ◽

Cited By ~ 1

Author(s):

Madeline Crews ◽

Brant Jones ◽

Kaitlyn Myers ◽

Laura Taalman ◽

Michael Urbanski ◽

...

Keyword(s):

Decision Making ◽

Classical Case ◽

Secretary Problem ◽

Sequential Decision Making ◽

Opportunity Costs ◽

Sequential Decision ◽

Probability Of Success ◽

Uniform Model ◽

Weighted Model ◽

Over Time

The game of best choice, also known as the secretary problem, is a model for sequential decision making with many variations in the literature. Notably, the classical setup assumes that the sequence of candidate rankings is uniformly distributed over time and that there is no expense associated with the candidate interviews. Here, we weight each ranking permutation according to the position of the best candidate in order to model costs incurred from conducting interviews with candidates that are ultimately not hired. We compare our weighted model with the classical (uniform) model via a limiting process. It turns out that imposing even infinitesimal costs on the interviews results in a probability of success that is about 28%, as opposed to 1/e (about 37%) in the classical case.

Download Full-text

A MIXED-CLUSTER INTERPRETATION OF THE METASTABLE ZONE IN FLUX GROWTH [Abstract]

Journal of the Magnetics Society of Japan ◽

10.3379/jmsjmag.11.s1_211 ◽

1987 ◽

Vol 11 (S_1_ISMO) ◽

pp. S1_211-211

Author(s):

Barbara WANKLYN

Keyword(s):

Flux Growth ◽

Mixed Cluster ◽

Metastable Zone

Download Full-text

Likelihood Approach for Bayesian Logistic Weighted Model

Cihan University-Erbil Scientific Journal ◽

10.24086/cuesj.v4n2y2020.pp9-12 ◽

2020 ◽

Vol 4 (2) ◽

pp. 9-12

Author(s):

Dler H. Kadir

Keyword(s):

Cognitive Development ◽

Case Analysis ◽

Inverse Probability Weighting ◽

Complete Case Analysis ◽

Parameter Estimates ◽

Probability Weighting ◽

Complete Case ◽

Inverse Probability ◽

Very Preterm ◽

Weighted Model

Increasing the response rate and minimizing non-response rates represent the primary challenges to researchers in performing longitudinal and cohort research. This is most obvious in the area of paediatric medicine. When there are missing data, complete case analysis makes findings biased. Inverse Probability Weighting (IPW) is one of many available approaches for reducing the bias using a complete case analysis. Here, a complete case is weighted by probability inverse of complete cases. The data of this work is collected from the neonatal intensive care unit at Erbil maternity hospital for the years 2012 to 2017. In total, 570 babies (288 male and 282 females) were born very preterm. The aim of this paper is to use inverse probability weighting on the Bayesian logistic model developmental outcome. The Mental Development Index (MDI) approach is used for assessing the cognitive development of those born very preterm. Almost half of the information for the babies was missing, meaning that we do not know whether they have cognitive development issues or they have not. We obtained greater precision in results and standard deviation of parameter estimates which are less in the posterior weighted model in comparison with frequent analysis.

Download Full-text