scholarly journals Multivariate Fractional Regression Estimation of Econometric Share Models

2015 ◽  
Vol 4 (1) ◽  
Author(s):  
John Mullahy
Keyword(s):  
Regression Models ◽  
Regression Estimation ◽  
Participation Rates ◽  
Boundary Values ◽  
Conditional Mean ◽  
Econometric Methods ◽  
Share Data ◽  
Mean Structures ◽  
Fractional Response ◽  
Response Variables

AbstractThis paper describes and applies econometric strategies for estimating regression models of economic share data outcomes where the shares may take boundary values (zero and 1) with nontrivial probability. The main focus of the paper is on the conditional mean structures of such data. The paper proposes an extension of the fractional regression methodology proposed by (Papke, L. E., and J. M. Wooldridge. 1996. “Econometric Methods for Fractional Response Variables with an Application to 401(k) Plan Participation Rates.”

Ochrona dziedzictwa kulturowego a działania na dużych szybkozmiennych zbiorach danych – perspektywa kryminologiczna

2019 ◽  
pp. 5-22
Author(s):  
Szymon Buczyński
Keyword(s):  
New Technology ◽  
Big Data Analytics ◽  
Technological Innovations ◽  
Cultural Organizations ◽  
Econometric Methods ◽  
The Arts ◽  
Huge Data ◽  
Wide Range ◽  
Share Data ◽  
Processing Techniques

Recent technological revolutions in data and communication systemsenable us to generate and share data much faster than ever before. Sophisticated data tools aim to improve knowledge and boost confdence. That technological tools will only get better and user-friendlier over the years, big datacan be considered an important tool for the arts and culture sector. Statistical analysis, econometric methods or data mining techniques could pave theway towards better understanding of the mechanisms occurring on the artmarket. Moreover crime reduction and prevention challenges in today’sworld are becoming increasingly complex and are in need of a new techniquethat can handle the vast amount of information that is being generated. Thisarticle provides an examination of a wide range of new technological innovations (IT) that have applications in the areas of culture preservation andheritage protection. The author provides a description of recent technological innovations, summarize the available research on the extent of adoptionon selected examples, and then review the available research on the eachform of new technology. Furthermore the aim of this paper is to explore anddiscuss how big data analytics affect innovation and value creation in cultural organizations and shape consumer behavior in cultural heritage, arts andcultural industries. This paper discusses also the likely impact of big dataanalytics on criminological research and theory. Digital criminology supports huge data base in opposition to conventional data processing techniques which are not only in suffcient but also out dated. This paper aims atclosing a gap in the academic literature showing the contribution of a bigdata approach in cultural economics, policy and management both froma theoretical and practice-based perspective. This work is also a startingpoint for further research.

scholarly journals Mapping Variation in Breast Cancer Screening: Where to Intervene?

2019 ◽  
Vol 16 (13) ◽  
pp. 2274 ◽  
Author(s):  
Cindy M. Padilla ◽  
François Painblanc ◽  
Patricia Soler-Michel ◽  
Veronica M. Vieira
Keyword(s):  
Mammography Screening ◽  
Regression Models ◽  
Screening Programme ◽  
Spatial Regression ◽  
Spatial Analyses ◽  
Participation Rates ◽  
Site Specific ◽  
Suburban Areas ◽  
Screening Participation ◽  
The Impact

Small geographic areas with lower mammography screening participation rates may reflect gaps in screening efforts. Our objective was to use spatial analyses to understand disparities in mammography screening use and to identify factors to increase its uptake in areas that need it in Lyon metropolitan area, France. Data for screened women between the ages of 50 and 74 were analyzed. Census blocks of screened and non screened women were extracted from the mammography screening programme 2015–2016 dataset. We used spatial regression models, within a generalized additive framework to determine clusters of census blocks with significantly higher prevalence of non-participation of mammography screening. Smoothed risk maps were crude and adjusted on the following covariates: deprivation index and opportunistic screening. Among 178,002 women aged 50 to 74, 49.9% received mammography screening. As hypothesized, women living in highly deprived census blocks had lower participation rates compared to less deprived blocks, 45.2% vs. 51.4% p < 0.001. Spatial analyses identified four clusters, one located in an urban area and three in suburban areas. Moreover, depending on the location of the cluster, the influence came from different variables. Knowing the impact of site-specific risk factors seems to be important for implementing an appropriate prevention intervention.

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