Department of Biostatistics and Epidemiology
http://hdl.handle.net/10675.2/313784
2024-03-26T02:10:25ZStatistical Methods for reaction Networks
http://hdl.handle.net/10675.2/623454
Statistical Methods for reaction Networks
Odubote, Oluseyi Samuel
Stochastic reaction networks are important tools for modeling many biological phenomena, and understanding these networks is important in a wide variety of applied research, such as in disease treatment and in drug development. Statistical inference about the structure and parameters of reaction networks, sometimes referred to in this setting as model calibration, is often challenging due to
intractable likelihoods. Here we utilize an idea similar to that of generalized estimating equations (GEE), which in this context are the so-called martingale estimating equations, for estimation of reaction rates of the network. The variance component is estimated using the approximate variance under the linear noise approximation, which is based on partial dierential equation, or Fokker-Planck
equations, which provides an approximation to the exact chemical master equation. The method is applied to data from the plague outbreak at Eyam, England from 1665-1666 and the COVID-19 pandemic data. We show empirically that the proposed method gives good estimates of the parameters in a large volume setting and works well in small volume settings.
Record is embargoed until 08/03/2021
False coverage rate - adjusted smoothed bootstrap simultaneous confidence intervals for selected parameters
http://hdl.handle.net/10675.2/623262
False coverage rate - adjusted smoothed bootstrap simultaneous confidence intervals for selected parameters
Sun, Jing
Many modern applications refer to a large number of populations with high dimensional parameters. Since there are so many parameters, researchers often draw inferences regarding the most significant parameters, which are called selected parameters. Benjamini and Yekutieli (2005) proposed the false coverage-statement rate (FCR) method for multiplicity correction when constructing confidence intervals for only selected parameters. FCR for the confidence interval method is parallel to the concept of the false discovery rate for multiple hypothesis testing. In practice, we typically construct FCR-adjusted approximate confidence intervals for selected parameters either using the bootstrap method or the normal approximation method. However, these approximated confidence intervals show higher FCR for small and moderate sample sizes. Therefore, we suggest a novel procedure to construct simultaneous confidence intervals for the selected parameters by using a smoothed bootstrap procedure. We consider a smoothed bootstrap procedure using a kernel density estimator. A pertinent problem associated with the smoothed bootstrap approach is how to choose the unknown bandwidth in some optimal sense. We derive an optimal choice for the bandwidth and the resulting smoothed bootstrap confidence intervals asymptotically to give better control of the FCR than its competitors. We further show that the suggested smoothed bootstrap simultaneous confidence intervals are FCR-consistent if the dimension of data grows no faster than N^3/2. Finite sample performances of our method are illustrated based on empirical studies. Through these empirical studies, it is shown that the proposed method can be successfully applied in practice.
This record is embargoed until 04/23/2021.
2020-05-01T00:00:00ZAn Iterative Procedure to Select and Estimate Wavelet-Based Functional Linear Mixed-Effects Regression Models
http://hdl.handle.net/10675.2/622784
An Iterative Procedure to Select and Estimate Wavelet-Based Functional Linear Mixed-Effects Regression Models
Lundeen, Jordan Sarah
Actigraphy is the continuous long-term measurement of activity-induced
acceleration by means of a portable device that often resembles a watch and is
typically worn on the wrist. Actigraphy is increasingly being used in clinical
research to measure sleep and activity rhythms that might not otherwise be
available using traditional techniques such as polysomnography. Actigraphy has
been shown to be of value when assessing circadian rhythm disorders and sleep
disorders and when evaluating treatment outcomes. It can provide more objective
information on sleep habits in the patient's natural sleep environment than using
the patient's recollection of their activity or a written sleep diary.
We propose a wavelet-based functional linear mixed model to investigate the
impact of functional predictors on a scalar response when repeated measurements
are available on multiple subjects. The advantage of the proposed model is that
each subject has both individual scalar covariate effects and individual functional
effects over time, while also sharing common population scalar covariate effects and
common population slope functions. An iterative procedure is used to estimate and
select the fixed and random effects by utilizing the partial consistency property of
the random effect coefficients and selecting groups of random effects simultaneously
via the smoothly clipped absolute deviation (SCAD) penalty function. In the first
study of its kind, we compare multiple functional regression methods through a large
number of simulation parameter combinations. The proposed model is applied to
actigraphy data to investigate the effect of daily activity on Hamilton Rating of
Depression Scale (HRSD), Insomnia Severity Index (ISI) and Reduced Morningness-
Eveningness Questionnare (RMEQ) scores.
2019-12-01T00:00:00ZTWO-SAMPLE TESTS FOR HIGH DIMEMSIONAL MEANS WITH PREPIVOTING and DATA TRANSFORMATION
http://hdl.handle.net/10675.2/622025
TWO-SAMPLE TESTS FOR HIGH DIMEMSIONAL MEANS WITH PREPIVOTING and DATA TRANSFORMATION
Hellebuyck, Rafael Adriel
Within the medical field, the demand to store and analyze small sample, large variable data has become ever-abundant. Several two-sample tests for equality of means, including the revered Hotelling’s T2 test, have already been established when the combined sample size of both populations exceeds the dimension of the variables. However, tests such as Hotelling’s T2 become either unusable or output small power when the number of variables is greater than the combined sample size. We propose a test using both prepivoting and Edgeworth expansion that maintains high power in this higher dimensional scenario, known as the “large p small n ” problem. Our test’s finite sample performance is compared with other recently proposed tests designed to also handle the “large p small n ” situation. We apply our test to a microarray gene expression data set and report competitive rates for both power and Type-I error.
The file you are attempting to access is currently restricted to Augusta University. Please log in with your NetID if off campus.
2019-01-08T00:00:00Z