scholarly journals Assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints

2021 ◽  
Author(s):  
Víctor M. Santos ◽  
Thomas Wahl ◽  
Robert Jane ◽  
Shubhra K. Misra ◽  
Kathleen D. White
Keyword(s):  
Statistical Models ◽  
Estuarine System ◽  
Multivariate Statistical ◽  
Data Constraints

Engagement Through the Diaspora Channel

2017 ◽  
Author(s):  
Michael S. Danielson
Keyword(s):  
Civil Society ◽  
Statistical Models ◽  
State Level ◽  
The State ◽  
Multivariate Statistical ◽  
Political Factors ◽  
Data Set ◽  
Level Data ◽  
Municipal Level

The first empirical task is to identify the characteristics of municipalities which US-based migrants have come together to support financially. Using a nationwide, municipal-level data set compiled by the author, the chapter estimates several multivariate statistical models to compare municipalities that did not benefit from the 3x1 Program for Migrants with those that did, and seeks to explain variation in the number and value of 3x1 projects. The analysis shows that migrants are more likely to contribute where migrant civil society has become more deeply institutionalized at the state level and in places with longer histories as migrant-sending places. Furthermore, the results suggest that political factors are at play, as projects have disproportionately benefited states and municipalities where the PAN had a stronger presence, with fewer occurring elsewhere.

DECISION MAKING BY VARIABLE CONTRIBUTION IN DISCRIMINANT, LOGIT, AND REGRESSION ANALYSES

2004 ◽  
Vol 03 (02) ◽  
pp. 265-279 ◽  
Author(s):  
STAN LIPOVETSKY ◽  
MICHAEL CONKLIN
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Statistical Models ◽  
Regression Analyses ◽  
Discriminant Functions ◽  
Multivariate Statistical ◽  
Linear Discriminant ◽  
Variable Contribution ◽  
Linear Aggregate

Comparative contribution of predictors in multivariate statistical models is widely used for decision making on the importance of the variables for the aims of analysis and prediction. However, the analysis can be made difficult because of the predictors' multicollinearity that distorts estimates for coefficients in the linear aggregate. To solve the problem of the robust evaluation of the predictors' contribution, we apply the Shapley Value regression analysis that provides consistent results in the presence of multicollinearity both for regression and discriminant functions. We also show how the linear discriminant function can be constructed as a multiple regression, and how the logistic regression can be approximated by linear regression that helps to obtain the variables contribution in the linear aggregate.

Measuring Threat and Violence

2018 ◽  
pp. 43-56
Author(s):  
Jeffrey S. Kopstein ◽  
Jason Wittenberg
Keyword(s):  
Statistical Models ◽  
Census Data ◽  
Location Information ◽  
Ecological Inference ◽  
Multivariate Statistical ◽  
Original Dataset ◽  
Geographical Unit ◽  
Nationalist Parties ◽  
Electoral Data

This chapter describes our data and methods. Our analysis is based on an original dataset of census returns, electoral results, and pogrom location information. We gathered these data at the lowest geographical unit for which they could be merged, yielding observations for over 2,000 localities. We use census data on religion and electoral data on support for Jewish and non-Jewish nationalist parties to measure the degree of perceived political threat prior to the outbreak of war. We establish the characteristics of those localities where pogroms occurred using a variety of methods, including multivariate statistical models and ecological inference.

Multivariate statistical models for disruption prediction at ASDEX Upgrade

2013 ◽  
Vol 88 (6-8) ◽  
pp. 1297-1301 ◽  
Author(s):  
R. Aledda ◽  
B. Cannas ◽  
A. Fanni ◽  
G. Sias ◽  
G. Pautasso
Keyword(s):  
Statistical Models ◽  
Multivariate Statistical
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