New Perspectives in Statistical Modeling and Data Analysis: Proceedings of the 7th Conference of the Classification and Data Analysis Group of the Italian Statistical Society, Catania, September 9-11, 2009 by Salvatore Ingrassia, Roberto Rocci, Maurizio V

2013 ◽  
Vol 81 (3) ◽  
pp. 460-461
Author(s):  
Fabrizio Durante
Keyword(s):  
Data Analysis ◽  
Statistical Modeling ◽  
Analysis Group

Data Analysis in Context-Based Statistical Modeling in Predictive Analytics

2021 ◽  
pp. 96-114
Author(s):  
Selvan C. ◽  
S. R. Balasundaram
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Statistical Modeling ◽  
Negative Correlation ◽  
Predictive Analytics ◽  
Mathematical Representation ◽  
Statistical Techniques ◽  
Key Factors ◽  
Dependent Variables ◽  
Future Outcomes

Data analysis is a process of studying, removing non-required data in the view level, and converting to needed patterns for sub decisions to make an aggregated decision. Statistical modeling is the process of applying statistical techniques in data analysis for taking proactive decisions depend requirements. The statistical modeling identifies relationship between variables, and it encompasses inferential statistics for model validation. The focus of the chapter is to analyze statistical modeling techniques in different contexts to understand the mathematical representation of data. The correlation and regression are used for analyzing association between key factors of companies' activities. Especially in business, correlation describes positive and negative correlation variables for analyzing the factors of business for supporting the decision-making process. The key factors are related with independent variables and dependent variables, which create cause and effect models to predict the future outcomes.

Multivariate Statistical Modeling and Data Analysis.

1989 ◽  
Vol 84 (407) ◽  
pp. 847
Author(s):  
Joseph S. Verducci ◽  
H. Bozdogan ◽  
A. K. Gupta
Keyword(s):  
Data Analysis ◽  
Statistical Modeling ◽  
Multivariate Statistical

scholarly journals Photoelectric Astrolabe and Astrolabe Star Catalogues

1986 ◽  
Vol 109 ◽  
pp. 375-377
Author(s):  
Luo Ding-jiang ◽  
Li Dong-ming
Keyword(s):  
Data Analysis ◽  
International Cooperation ◽  
Analysis Group

At the Shaanxi Observatory, regular observations with the prototype of the photoelectric astrolabe PAI have been carried out since 1973. Three photoelectric astrolabes of type Mark II (PAII) were mounted at the Shanghai, Beijing and Yunnan Observatories in 1974, 1976 and 1978, respectively. Details of the instruments and the results of the observation were published previously. (Photoelectric Astrolabe Developing Group 1973 and 1975, Second Group, First Divsion Shaanxi Observatory 1974, Astrolabe Data Analysis Group, First Division, Shaanxi Observatory 1975, Wang, L-j 1979, Astrolabe Group, First Division, Shanghai Observatory 1976, Wang, L-z and Luo 1979, Lu and Luo 1979). Time and latitude determinations by these sets of photoelectric astrolabes form a part in the international cooperation of BIH, IPMS as well as in the short campaign of project MERIT.

scholarly journals A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis

Risks ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 32 ◽  
Author(s):  
José María Sarabia ◽  
Faustino Prieto ◽  
Vanesa Jordá ◽  
Stefan Sperlich
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Stock Returns ◽  
Prior Knowledge ◽  
Statistical Modeling ◽  
Financial Data ◽  
Adaptive Inference ◽  
Data Adaptive ◽  
Financial Data Analysis ◽  
Global And Local

This note revisits the ideas of the so-called semiparametric methods that we consider to be very useful when applying machine learning in insurance. To this aim, we first recall the main essence of semiparametrics like the mixing of global and local estimation and the combining of explicit modeling with purely data adaptive inference. Then, we discuss stepwise approaches with different ways of integrating machine learning. Furthermore, for the modeling of prior knowledge, we introduce classes of distribution families for financial data. The proposed procedures are illustrated with data on stock returns for five companies of the Spanish value-weighted index IBEX35.

Introduction

Geophysics ◽  
1967 ◽  
Vol 32 (3) ◽  
pp. 415-417
Author(s):  
Sven Treurel ◽  
Enders A. Robinson
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Research Program ◽  
Seismic Data ◽  
Financial Support ◽  
Massachusetts Institute ◽  
Massachusetts Institute Of Technology ◽  
Analysis Group ◽  
Full Participation ◽  
Institute Of Technology

In 1950, a small research project concerned with the application of the theory of time series to seismic data analysis was formed within the Mathematics Department of the Massachusetts Institute of Technology. This early work was pursued by Dr. E. A. Robinson and by Professor G. P. Wadsworth. The results of these studies were considered promising, and by 1952 a number of oil and geophysical exploration companies had been approached in order to determine their interest in supporting an expanded research program in this area. Eventually a group of these companies agreed to participate in such an effort, and in February 1953 the MIT Geophysical Analysis Group (GAG) was organized within the Department of Geology and Geophysics. Full participation in the activities of the GAG was open at any time to all interested companies. All members provided annual financial support, and a number of them furnished the GAG with data for analysis.

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