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- Carsten H. Botts, Michael J. Daniels
- Computational Statistics & Data Analysis
- 2008

We model sparse functional data from multiple subjects with a mixed-effects regression spline. In this model, the expected values for any subject (conditioned on the random effects) can be written as the sum of a population curve and a subject-specific deviate from this population curve. The population curve and the subject-specific deviates are both… (More)

- Carsten H. Botts, Wolfgang Hörmann, Josef Leydold
- Statistics and Computing
- 2013

The acceptance-rejection algorithm is often used to sample from non-standard distributions. For this algorithm to be efficient, however, the user has to create a hat function that majorizes and closely matches the density of the distribution to be sampled from. There are many methods for automatically creating such hat functions, but these methods require… (More)

We propose a shrinkage estimator for spectral densities based on a multilevel normal hierarchical model. The first level captures the sampling variability via a likelihood constructed using the asymptotic properties of the periodogram. At the second level, the spectral density is shrunk towards a parametric time series model. To avoid selecting a particular… (More)

- J. D. Opsomer, Z. Wu, +5 authors C. Botts
- 2001

This article describes the sampling design, survey methodology and findings of a natural resources survey conducted in the Rathbun Lake Watershed in Southern Iowa in 1999-2000. The goal of the survey was to quantify the erosion from all sources on agricultural lands and the ecological health of streams for each of 61 subwatersheds in the area. A total of… (More)

Description A universal non-uniform random number generator for quite arbitrary distributions with piecewise twice differentiable densities.

- Carsten Botts
- 2010

The need to simulate from a positive multivariate normal distribution arises in several settings, specifically in Bayesian analysis. A variety of algorithms can be used to sample from this distribution, but most of these algorithms involve Gibbs sampling. Since the sample is generated from a Markov chain, the user has to account for the fact that sequential… (More)

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