Conformal Prediction Classification of a Large Data Set of Environmental Chemicals from ToxCast and Tox21 Estrogen Receptor Assays

2016 ◽  
Vol 29 (6) ◽  
pp. 1003-1010 ◽  
Author(s):  
Ulf Norinder ◽  
Scott Boyer
Keyword(s):  
Estrogen Receptor ◽  
Large Data ◽  
Environmental Chemicals ◽  
Data Set ◽  
Conformal Prediction ◽  
Large Data Set ◽  
Receptor Assays

Identifying Bands in the Knowledge Exchange Spectrum in an Online Health Infomediary

2015 ◽  
Vol 10 (3) ◽  
pp. 63-84 ◽  
Author(s):  
Dobin Yim ◽  
Jiban Khuntia ◽  
Young Argyris
Keyword(s):  
Cluster Analysis ◽  
Cosmetic Surgery ◽  
Knowledge Exchange ◽  
Large Data ◽  
Two Dimensions ◽  
Data Set ◽  
Large Data Set ◽  
Similarities And Differences

Online health infomediaries have the objective of knowledge exchange between participants. Visitor contribution is an important factor for the success of the infomediaries. Providers engaged with infomediaries need visitor identification for reputational incentives. However, identification or classification of visitors in online health infomediaries is sparse in literature. This study proposes two dimensions of participation, the intention and intensity levels of visitors, to conceptualize four user categories: community supporters, experiencer providers, knowledge questors, and expertise contributors. The authors validate these categories using a unique large data set collected from a health infomediary for cosmetic surgery, and consisting of 162,598 observed activities of 44,350 visitors, at different participation levels in the year 2012-13. They use cluster analysis to describe similarities and differences among the four user categories. Practice implications are discussed.

User Behaviors and Knowledge Exchange in Health Infomediary

2018 ◽  
pp. 213-233
Author(s):  
Dobin Yim ◽  
Jiban Khuntia ◽  
Young Anna Argyris
Keyword(s):  
Cluster Analysis ◽  
Cosmetic Surgery ◽  
Knowledge Exchange ◽  
Large Data ◽  
Two Dimensions ◽  
Data Set ◽  
User Behaviors ◽  
Large Data Set ◽  
Similarities And Differences

Online health infomediaries have the objective of knowledge exchange between participants. Visitor contribution is an important factor for the success of the infomediaries. Providers engaged with infomediaries need visitor identification for reputational incentives. However, identification or classification of visitors in online health infomediaries is sparse in literature. This chapter proposes two dimensions of participation, the intention and intensity levels of visitors, to conceptualize four user categories: community supporters, experiencer providers, knowledge questors, and expertise contributors. The authors validate these categories using a unique large data set collected from a health infomediary for cosmetic surgery, and consisting of 162,598 observed activities of 44,350 visitors, at different participation levels in the year 2012-13. They use cluster analysis to describe similarities and differences among the four user categories. Practice implications are discussed.

Climatology of nutrient distributions in the South China Sea based on a large data set derived from a new algorithm

2021 ◽  
pp. 102586
Author(s):  
Chuanjun Du ◽  
Ruoying He ◽  
Zhiyu Liu ◽  
Tao Huang ◽  
Lifang Wang ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
Large Data ◽  
The South China Sea ◽  
The South ◽  
Data Set ◽  
China Sea ◽  
Large Data Set

scholarly journals Spike detection: Inter-reader agreement and a statistical Turing test on a large data set

2017 ◽  
Vol 128 (1) ◽  
pp. 243-250 ◽  
Author(s):  
Mark L. Scheuer ◽  
Anto Bagic ◽  
Scott B. Wilson
Keyword(s):  
Large Data ◽  
Turing Test ◽  
Spike Detection ◽  
Data Set ◽  
Large Data Set

Main large data set features detection by a linear predictor model

2014 ◽  
Author(s):  
Carlos Enrique Gutierrez ◽  
Prof. Mohamad Reza Alsharif ◽  
Mahdi Khosravy ◽  
Prof. Katsumi Yamashita ◽  
Prof. Hayao Miyagi ◽  
...  
Keyword(s):  
Large Data ◽  
Data Set ◽  
Linear Predictor ◽  
Large Data Set ◽  
Predictor Model

Governance Problems in Closely Held Corporations

2011 ◽  
Vol 46 (4) ◽  
pp. 943-966 ◽  
Author(s):  
Venky Nagar ◽  
Kathy Petroni ◽  
Daniel Wolfenzon
Keyword(s):  
Ownership Structure ◽  
Large Data ◽  
Minority Shareholders ◽  
Return On Assets ◽  
Data Set ◽  
Large Data Set ◽  
Ownership Rights ◽  
Shared Ownership

AbstractA major governance problem in closely held corporations is the majority shareholders’ expropriation of minority shareholders. As a solution, legal and finance research recommends that the main shareholder surrender some control to minority shareholders via ownership rights. We test this proposition on a large data set of closely held corporations. We find that shared-ownership firms report a substantially larger return on assets and lower expense-to-sales ratios. These findings are robust to institutionally motivated corrections for endogeneity of ownership structure. We provide evidence on the presence of governance problems and the effectiveness of shared ownership as a solution in settings characterized by illiquidity of ownership.

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