Zobrazeno 1 - 10
of 177
pro vyhledávání: '"Clarke, Bertrand"'
We present two new approaches for point prediction with streaming data. One is based on the Count-Min sketch (CMS) and the other is based on Gaussian process priors with a random bias. These methods are intended for the most general predictive proble
Externí odkaz:
http://arxiv.org/abs/2408.01318
Autor:
Clarke, Bertrand, Dustin, Dean
We use the law of total variance to generate multiple expressions for the posterior predictive variance in Bayesian hierarchical models. These expressions are sums of terms involving conditional expectations and conditional variances. Since the poste
Externí odkaz:
http://arxiv.org/abs/2406.11806
Autor:
Dustin, Dean, Clarke, Bertrand
We give two prediction intervals (PI) for Generalized Linear Models that take model selection uncertainty into account. The first is a straightforward extension of asymptotic normality results and the second includes an extra optimization that improv
Externí odkaz:
http://arxiv.org/abs/2305.15579
Autor:
Clarke, Bertrand, Yao, Yuling
This paper reviews the growing field of Bayesian prediction. Bayes point and interval prediction are defined and exemplified and situated in statistical prediction more generally. Then, four general approaches to Bayes prediction are defined and we t
Externí odkaz:
http://arxiv.org/abs/2304.12218
Autor:
Dustin, Dean, Clarke, Bertrand
We give a decomposition of the posterior predictive variance using the law of total variance and conditioning on a finite dimensional discrete random variable. This random variable summarizes various features of modeling that are used to form the pre
Externí odkaz:
http://arxiv.org/abs/2209.00636
Choosing a shrinkage method can be done by selecting a penalty from a list of pre-specified penalties or by constructing a penalty based on the data. If a list of penalties for a class of linear models is given, we provide comparisons based on sample
Externí odkaz:
http://arxiv.org/abs/2201.02244
Autor:
Mpoudeu, Merlin, Clarke, Bertrand
We derive an objective function that can be optimized to give an estimator of the Vapnik- Chervonenkis dimension for model selection in regression problems. We verify our estimator is consistent. Then, we verify it performs well compared to seven oth
Externí odkaz:
http://arxiv.org/abs/1808.05296
There is a growing awareness of the important roles that microbial communities play in complex biological processes. Modern investigation of these often uses next generation sequencing of metagenomic samples to determine community composition. We pro
Externí odkaz:
http://arxiv.org/abs/1801.07765