Daniel Kowal is an assistant professor in the  Department of Statistics  at Rice University.

Daniel Kowal is an assistant professor in the Department of Statistics at Rice University.

I am interested in developing innovative statistical methodology for massive data sets with complex dependence structures, including functional, time series, and spatial data. For many applications, these dependence structures appear concurrently.

With my research, I seek to directly and meaningfully address open questions in important fields such as economics, public health and policy, biomedical engineering, finance, and astronomy.  

 


Recent News:

Install and load using the following code:

library(devtools)

devtools::install_github("drkowal/fosr")

library(fosr)

Uses include:

  1. Bayesian estimation and inference for function-on-scalar regression: fosr(...) 

  2. Decision-theoretic approach to variable selection in functional regression: fosr_select()

  3. Additional tools for plotting, simulations, and evaluation of model fit

Install and load using the following code:

library(devtools)

devtools::install_github("drkowal/dsp")

library(dsp)

Uses include:

  1. Curve-fitting of irregular data via Bayesian trend filtering: btf(...)

  2. An adaptive time-varying parameter regression model: btf_reg(...)

  3. Curve-fitting of irregular data with unequally-spaced observations: btf_bpsline(...)