Parsimonious graphical dependence models constructed from vines

2018 ◽  
Vol 46 (4) ◽  
pp. 532-555
Author(s):  
Harry Joe
Keyword(s):  
Graphical Dependence ◽  
Dependence Models

Daily Labor Supply and Adaptive Reference Points

2021 ◽  
Vol 111 (8) ◽  
pp. 2417-2443
Author(s):  
Neil Thakral ◽  
Linh T. Tô
Keyword(s):  
Labor Supply ◽  
Reference Point ◽  
Structural Model ◽  
Reference Points ◽  
Degree Of Freedom ◽  
Reference Dependence ◽  
Field Evidence ◽  
Dynamic View ◽  
Expected Earnings ◽  
Dependence Models

This paper provides field evidence on how reference points adjust, a degree of freedom in reference-dependence models. Examining this in the context of cabdrivers’ daily labor-supply behavior, we ask how the within-day timing of earnings affects decisions. Drivers work less in response to higher accumulated income, with a strong effect for recent earnings that gradually diminishes for earlier earnings. We estimate a structural model in which drivers work toward a reference point that adjusts to deviations from expected earnings with a lag. This dynamic view of reference dependence reconciles conflicting “neoclassical” and “behavioral” interpretations of evidence on daily labor-supply decisions. (JEL J22, J31, L94)

Semantic Graphical Dependence Parsing Model in Improving English Teaching Abilities

2021 ◽  
Vol 20 (3) ◽  
pp. 1-14
Author(s):  
Erlu Wang ◽  
Priyan Malarvizhi Kumar ◽  
R. Dinesh Jackson samuel
Keyword(s):  
Difficult Problem ◽  
High Order ◽  
Computer Applications ◽  
Data Systems ◽  
Beam Search ◽  
Graphical Dependence ◽  
Large Size ◽  
Comparable Accuracy ◽  
Bilingual Text ◽  
Dependency Model

It is a very difficult problem to achieve high-order functionality for graphical dependency parsing without growing decoding difficulties. To solve this problem, this article offers a way for Semantic Graphical Dependence Parsing Model (SGDPM) with a language-dependency model and a beam search to represent high-order functions for computer applications. The first approach is to scan a large amount of unnoticed data using a baseline parser. It will build auto-parsed data to create the Language-dependence Model (LDM). The LDM is based on a set of new features during beam search decoding, where it will incorporate the LDM features into the parsing model and utilize the features in parsing models of bilingual text. Our approach has main benefits, which include rich high-order features that are described given the large size and the additional large crude corpus for increasing the difficulty of decoding.  Further, SGDPM has been evaluated using the suggested method for parsing tasks of mono-parsing text and bi-parsing text to carry out experiments on the English and Chinese data in the mono-parsing text function using computer applications. Experimental results show that the most accurate Chinese data is obtained with the best known English data systems and their comparable accuracy. Furthermore, the lab-scale experiments on the Chinese/General bilingual information in the bitext parsing process outperform the best recorded existing solutions.

Explaining the Escalation of Drug Use in Substance Dependence: Models and Appropriate Animal Laboratory Tests

Pharmacology ◽  
2007 ◽  
Vol 80 (2-3) ◽  
pp. 65-119 ◽  
Author(s):  
Gerald Zernig ◽  
Serge H. Ahmed ◽  
Rudolf N. Cardinal ◽  
Drake Morgan ◽  
Elio Acquas ◽  
...  
Keyword(s):  
Drug Use ◽  
Laboratory Tests ◽  
Substance Dependence ◽  
Dependence Models

Spatiotemporal generation of long-range dependence models and estimation

2006 ◽  
Vol 17 (2) ◽  
pp. 139-146 ◽  
Author(s):  
M. P. Frías ◽  
M. D. Ruiz-Medina ◽  
F. J. Alonso ◽  
J. M. Angulo
Keyword(s):  
Long Range ◽  
Long Range Dependence ◽  
Dependence Models

Knowledge Learning of Insurance Risks Using Dependence Models

2020 ◽  
Author(s):  
Zifeng Zhao ◽  
Peng Shi ◽  
Xiaoping Feng
Keyword(s):  
Large Scale ◽  
Predictive Analytics ◽  
Insurance Industry ◽  
Spatial Association ◽  
Capital Allocation ◽  
Risk Profile ◽  
Composite Likelihood ◽  
Multiple Sources ◽  
Property Insurance ◽  
Dependence Models

Learning the customers’ experience and behavior creates competitive advantages for any company over its rivals. The insurance industry is an essential sector in any developed economy and a better understanding of customers’ risk profile is critical to decision making in all aspects of insurance operations. In this paper, we explore the idea of using copula-based dependence models to learn the hidden risk of policyholders in property insurance. Specifically, we build a novel copula model to accommodate the dependence over time and over space among spatially clustered property risks. To tackle the computational challenge caused by the discreteness feature of large-scale insurance data, we propose an efficient multilevel composite likelihood approach for parameter estimation. Provided that latent risk induces correlation, the proposed customer learning method offers improved predictive analytics by allowing insurers to borrow strength from related risks in predicting new risks and also helps reveal the relative importance of the multiple sources of unobserved heterogeneity in updating policyholders’ risk profile. In the empirical study, we examine the loss cost of a portfolio of entities insured by a government property insurance program in Wisconsin. We find both significant temporal and spatial association among property risks. However, their effects on the predictive distribution of loss cost are different for the new and renewal policyholders. The two sources of dependence are complements for the former and substitutes for the latter. These findings are shown to have substantial managerial implications in key insurance operations such as experience rating, capital allocation, and reinsurance arrangement.

scholarly journals Regression analysis of the influence of auger surface hardness on its wear during dehydration of solid waste in a garbage truck

2021 ◽  
Vol 101 (3) ◽  
pp. 48-55
Author(s):  
O. Bereziuk ◽  
V. Savulyak ◽  
V. Kharzhevskyi ◽  
◽  
Keyword(s):  
Wear Resistance ◽  
Regression Analysis ◽  
Energy Consumption ◽  
Solid Waste ◽  
Surface Hardness ◽  
Household Waste ◽  
Graphical Dependence ◽  
Friction Path ◽  
Reduction Of Energy Consumption ◽  
The Cost

The article is dedicated to the study of the influence of the surface hardness of the auger on its wear during dehydration of solid waste in the garbage truck. Using the method of regression analysis, the logarithmic dependencies of auger wear depending on the hardness of its surface for different values of the friction path are determined. Graphical dependences of augur wear depending on the hardness of its surface for different values of the friction path are made up, and it confirms sufficient convergence of the obtained dependencies. Carried out additional regression analysis allowed to obtain the dependence of wear of the auger depending on the hardness of its surface and the friction path, which showed that during two weeks of operation and wear of the auger during dehydration of solid waste in the garbage truck increasing the surface hardness of the auger from 2310 MPa to 10050 MPa reducing the rate of growth of energy consumption of solid waste dehydration from 16.7 % to 1.5 %, and, consequently, to reduce the cost of the process of their dehydration in the garbage truck. The graphical dependence of the reduction of energy consumption of dehydration of solid household waste due to the increase in the hardness of the auger surface during its two-week wear is presented. It was established the expediency of further research to determine the rational material of the auger and ways to increase its wear resistance

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