Parametric Models of Periodogram

Parametric Models

Parametric vs Non - Parametric Generative Models

The contrast is exemplified by the following classification task. Let’s assume that we are given data, X , i.e. the observed variable and we want to determine its class label, Y, the unobserved (target) variable. A generative classifier, such as Naive Bayes, makes use of the joint distribution of X and Y, i.e. P(X ,Y) to perform this inference. While a discriminative classifier, such as a Logis...

Supplementary Materials to “Combining parametric, semi-parametric, and non-parametric survival models with stacked survival models”

Section 1 demonstrates the connection between the Brier Score in the absence of censoring versus the inverse probability-of-censoring weighted Brier Score. Section 2 derives the meansquared error decomposition presented in Section 3 of the main paper, and presents illustrations and examples regarding the impact of candidate survival models on performance. In addition, a simple example illustrat...

Combining parametric, semi-parametric, and non-parametric survival models with stacked survival models.

For estimating conditional survival functions, non-parametric estimators can be preferred to parametric and semi-parametric estimators due to relaxed assumptions that enable robust estimation. Yet, even when misspecified, parametric and semi-parametric estimators can possess better operating characteristics in small sample sizes due to smaller variance than non-parametric estimators. Fundamenta...

Identification in Parametric Models