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This is a reprint of articles from the Special Issue published online in the open access journal. Mathematics (ISSN 2227-7390) (available at: https://www.mdpi.
The book has been in print for more than 30 years, in a period of rapid change in statisti- cal methodology and particularly in statistical computing. ... guide ...
This series of high-quality upper-division textbooks and expository monographs cov- ers all aspects of stochastic applicable mathematics.
The purpose of this report is to document both the implemented sampling design and all corresponding data modeling and inference techniques used during the ...
Managing and monitoring statistical models can present formidable challenges when you have multiple models used by a team of analysts over time.
In regression analysis, statistical models are used to predict one variable (the outcome or dependent variable) from one or more other variables (the predictor ...
This paper addresses two closely related questions, “What is a statistical model?” and “What is a parameter?” The notions that a model must “make.
We introduce a regression-based nested Monte Carlo simulation method for the estimation of financial risk. An outer simulation level is used to generate ...
For this we use regression analysis where we see how well a straight line fits through the data, indicating how related the two measures are. As well as ...
Thus, this book introduces methods that combine and build upon those described by Lewis-Beck (1980) and Achen (1982) on regression and by Aldrich and. Nelson ( ...
Factor exposure analysis is a quantitative method that gives investors the ability to measure and understand the return drivers of investment strategies. In.
Estimation of simple statistical models such as linear and logistic regres- sions with survey data is well established and widely used. These models are however ...
Regression analysis treats all independent (X) variables in the analysis as numerical. Numerical variables are interval or ratio scale variables whose ...
Abstract. There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated.
