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Full-Text Articles in Physical Sciences and Mathematics
A Comparison Of The Predictive Ability Of Logistic Regression And Time Series Analysis On Business Credit Data, Lauren Staples
A Comparison Of The Predictive Ability Of Logistic Regression And Time Series Analysis On Business Credit Data, Lauren Staples
Published and Grey Literature from PhD Candidates
The credit industry creates models to determine the risk of lending money to consumers as well as to commercial customers. These models are heavily regulated in the U.S. as well as in other countries. Model inputs must be explainable to customers as well as to regulators. Two such modeling approaches that are currently commonly used are logistic regression models and time series models. This paper steps through the preprocessing and model building of these two models on a large commercial data set and compares the predictive ability of these two methods. The two models achieved similar accuracy results: the logistic …
Non-Stationary Counts With Mixture Distributions, Ziqiang Lin
Non-Stationary Counts With Mixture Distributions, Ziqiang Lin
Legacy Theses & Dissertations (2009 - 2024)
We study a new non--stationary mixture Pengram and thinning model for time series of counts that include the effect of covariate variables on the outcome variable. Properties of the model and performance are discussed. It has a simpler likelihood function than the non--stationary INAR(1) model and therefore MLE estimators for the model's parameters are easier to find. Therefore the model offers an alternative to non--stationary INAR(1).
Spatio-Temporal Frequency Separation With Application Of Kolmogorov-Zurbenko Filters To The Multivariate Analysis Of Melanoma Prevalence, Edward Valachovic
Spatio-Temporal Frequency Separation With Application Of Kolmogorov-Zurbenko Filters To The Multivariate Analysis Of Melanoma Prevalence, Edward Valachovic
Legacy Theses & Dissertations (2009 - 2024)
Time Series Analysis is the observation of variables recorded across time. Observations are visualized and analysis often performed in the native time domain. It is common for a time series to be the dependent variable of more than one factor. Several factors can have concurrent and combined effects. The time domain presents an obstacle due to constructive and destructive interference of factors at each time point. Unless effects are clearly pronounced and separable, the entanglement of factors along with the presence and intensity of random variation can obscure true relationships.