Bayesian spectral density estimation using P-splines with quantile-based knot placement

Flexible estimation in cure survival models using Bayesian P-splines

In the analysis of survival data, it is usually assumed that any unit will experience the event of interest if it is observed for a sufficient long time. However, one can explicitly assume that an unknown proportion of the population under study will never experience the monitored event. The promotion time model, which has a biological motivation, is one of the survival models taking this featu...

Bayesian P-Splines

Computational aspects of Bayesian spectral density estimation

Gaussian time-series models are often specified through their spectral density. Such models pose several computational challenges, in particular because of the non-sparse nature of the covariance matrix. We derive a fast approximation of the likelihood for such models. We use importance sampling to correct for the approximation error. We show that the variance of the importance sampling weights...

Using Quantile Smoothing Splines to

The goal of this paper is to provide a statistically based deenition of employment subcenters for multicentric urban areas. In particular, we examine the shape of the employment density function using quantile smoothing splines as a nonparametric empirical speciication. This approach allows inspection of the employment gradient at the upper tail rather than the center of the employment density ...

Density Estimation via Hybrid Splines

The Hybrid Spline method (H-spline) is a method of density estimation which involves regression splines and smoothing splines methods. Using basis functions (B-splines), this method is much faster than Smoothing Spline Density Estimation approach (Gu, 1993). Simulations suggest that with more structured data (e.g., several modes) H-spline method estimates the modes as well as Logspline (Kooperb...