Applied Missing Data Analysis
... analysis examples. The level with which to integrate specific software programs was an issue that presented me with a dilemma throughout the writing process. In the end, I chose to make the analysis examples independent of any program. In the 2 years that it took to write this book, software program ...
... analysis examples. The level with which to integrate specific software programs was an issue that presented me with a dilemma throughout the writing process. In the end, I chose to make the analysis examples independent of any program. In the 2 years that it took to write this book, software program ...
Statistics Catalog
... give an oral presentation at a forum to be approved by the faculty member. ELECTIVES (12 S.H.) In consultation with an advisor, students should choose 12 semester hours from MATH courses numbered 310 and above, STAT courses numbered 320 and above, CS courses numbered 234 and above, or FIN 335. Stude ...
... give an oral presentation at a forum to be approved by the faculty member. ELECTIVES (12 S.H.) In consultation with an advisor, students should choose 12 semester hours from MATH courses numbered 310 and above, STAT courses numbered 320 and above, CS courses numbered 234 and above, or FIN 335. Stude ...
Bayesian Variable Selection in Normal Regression Models
... In the Bayesian approach to variable selection prior distributions representing the subjective believes about parameters are assigned to the regressor coefficients. By applying Bayes’ rule they are updated by the data and converted into the posterior distributions, on which all inference is based on ...
... In the Bayesian approach to variable selection prior distributions representing the subjective believes about parameters are assigned to the regressor coefficients. By applying Bayes’ rule they are updated by the data and converted into the posterior distributions, on which all inference is based on ...
Value Investing in Credit Markets
... 2.3.1 Out-of-sample forecast accuracy of default The common approach in the academic literature to assess default forecasting models is via classification accuracy. We are interested in forecasting default in the next twelve months using data available in month t. Therefore, the „best‟ default fore ...
... 2.3.1 Out-of-sample forecast accuracy of default The common approach in the academic literature to assess default forecasting models is via classification accuracy. We are interested in forecasting default in the next twelve months using data available in month t. Therefore, the „best‟ default fore ...
Introduction to Modern Physics PHYX 2710
... measurements 1. Note the precision of our measurement is reflected in the estimated error which states what values we would expect to get if we repeated the measurement 2. Precision is defined as a measure of the reproducibility of a measurement 3. Such errors are called random (statistical) errors ...
... measurements 1. Note the precision of our measurement is reflected in the estimated error which states what values we would expect to get if we repeated the measurement 2. Precision is defined as a measure of the reproducibility of a measurement 3. Such errors are called random (statistical) errors ...
research paper series Research Paper 2005/25
... low corruption countries, an indication of multiple equilibria (Blackburn et al., 2002, 2005b). In this paper we search for evidence of a non-linear relationship between corruption and development as well as its causes and its empirical consequences using cross-country data for 87 countries from 198 ...
... low corruption countries, an indication of multiple equilibria (Blackburn et al., 2002, 2005b). In this paper we search for evidence of a non-linear relationship between corruption and development as well as its causes and its empirical consequences using cross-country data for 87 countries from 198 ...
X-bar Charts
... The strength of control charts comes from their ability to detect sudden changes in a process that result from the presence of assignable causes. Unfortunately, the X-bar chart is poor at detecting drifts (gradual trends) or small shifts in the process. For example, there might be a positive trend i ...
... The strength of control charts comes from their ability to detect sudden changes in a process that result from the presence of assignable causes. Unfortunately, the X-bar chart is poor at detecting drifts (gradual trends) or small shifts in the process. For example, there might be a positive trend i ...
Ridge Regression
... outliers), and independence. Since ridge regression does not provide confidence limits, normality need not be assumed. ...
... outliers), and independence. Since ridge regression does not provide confidence limits, normality need not be assumed. ...
A Survey of Exact Inference for Contingency Tables Alan Agresti Statistical Science
... for contingency tables in the context of loglinear models. Sections 2 and 3 focus on two-way tables, Section 2 for the 2 x 2 case and Section 3 for the I x J case. Section 4 focuses on three-way tables, with emphasis on the 2 x 2 x K case. Section 5 discusses exact inference for logistic regression ...
... for contingency tables in the context of loglinear models. Sections 2 and 3 focus on two-way tables, Section 2 for the 2 x 2 case and Section 3 for the I x J case. Section 4 focuses on three-way tables, with emphasis on the 2 x 2 x K case. Section 5 discusses exact inference for logistic regression ...
Cross-validation
... AIC and BIC are both maximum likelihood estimate driven and penalize free parameters in an effort to combat overfitting, they do so in ways that result in significantly different behavior. AIC = -2*ln(likelihood) + 2*k, k = model degrees of freedom BIC = -2*ln(likelihood) + ln(N)*k, k = model degree ...
... AIC and BIC are both maximum likelihood estimate driven and penalize free parameters in an effort to combat overfitting, they do so in ways that result in significantly different behavior. AIC = -2*ln(likelihood) + 2*k, k = model degrees of freedom BIC = -2*ln(likelihood) + ln(N)*k, k = model degree ...
