Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- Bayes inference. (1)
- Bayesian random-effects meta-analysis model (1)
- Biomass (1)
- Biometrics (1)
- Cancer genomics (1)
-
- Conditional Maximum Likelihood (CML) (1)
- Count time series (1)
- Crossing hazards (1)
- Crossover (1)
- Eugenics (1)
- Feature selection (1)
- Gene expression (1)
- Growth and yield (1)
- HSV (1)
- Harvesting (1)
- High-throughput "omics" (1)
- History of mathematics (1)
- Incomplete data (1)
- Integer-valued Auto-Regressive (INAR) process (1)
- Kullback-Leibler information divergence (1)
- Likelihood theory (1)
- Linear regression (1)
- Meta-analysis (1)
- Missing data mechanism (1)
- Mixed (1)
- NDL-INAR(1) stationary process (1)
- New Discrete Lindley (NDL) distribution (1)
- Non-proportional hazards (1)
- Overdispersion (1)
- Partial likelihood (1)
- Publication
- Publication Type
Articles 1 - 7 of 7
Full-Text Articles in Biometry
(R2174) A Stationary First-Order Autoregressive Process With New Discrete Lindley Marginal Distribution, Tégawendé Martin Kabore, Jean-Etienne Ouindllassida Ouédraogo
(R2174) A Stationary First-Order Autoregressive Process With New Discrete Lindley Marginal Distribution, Tégawendé Martin Kabore, Jean-Etienne Ouindllassida Ouédraogo
Applications and Applied Mathematics: An International Journal (AAM)
This paper introduces a new stationary first-order autoregressive integer-valued process with a marginal distribution following the New Discrete Lindley distribution, referred to as NDLINAR( 1). The process is developed to model over-dispersed count time series exhibiting a mixed behavior arising from the combination of multiple distributions. Its statistical properties are thoroughly investigated, and the parameters are estimated using conditional maximum likelihood. The asymptotic properties of the estimators are also analyzed. The performance of the proposed model is evaluated by comparing it with other existing INAR(1) processes through applications to real datasets. Results demonstrate that the NDL-INAR(1) process effectively captures the …
“Regression To The Mean”: The Confluence Of Eugenics And Statistics In The 19th And 20th Centuries, Emrys G. King
“Regression To The Mean”: The Confluence Of Eugenics And Statistics In The 19th And 20th Centuries, Emrys G. King
Pomona Senior Theses
The work of this thesis is twofold — first, qualitatively characterizing the confluence between the British eugenics and statistics movements in the late 19th and early 20th centuries, and second, quantitatively analyzing the effect of this foundation on pedagogical materials in the growing field of statistics between 1880 and 1970. Towards the first goal, the history of the method of least squares, state statistics, and positive and negative eugenics are outlined, followed by a close reading of the foundational texts authored by Francis Galton and Karl Pearson that introduced linear regression. Towards the latter goal, English-language statistics textbooks published between …
Unified Methods For Feature Selection In Large-Scale Genomic Studies With Censored Survival Outcomes, Lauren Spirko-Burns, Karthik Devarajan
Unified Methods For Feature Selection In Large-Scale Genomic Studies With Censored Survival Outcomes, Lauren Spirko-Burns, Karthik Devarajan
COBRA Preprint Series
One of the major goals in large-scale genomic studies is to identify genes with a prognostic impact on time-to-event outcomes which provide insight into the disease's process. With rapid developments in high-throughput genomic technologies in the past two decades, the scientific community is able to monitor the expression levels of tens of thousands of genes and proteins resulting in enormous data sets where the number of genomic features is far greater than the number of subjects. Methods based on univariate Cox regression are often used to select genomic features related to survival outcome; however, the Cox model assumes proportional hazards …
Models For Hsv Shedding Must Account For Two Levels Of Overdispersion, Amalia Magaret
Models For Hsv Shedding Must Account For Two Levels Of Overdispersion, Amalia Magaret
UW Biostatistics Working Paper Series
We have frequently implemented crossover studies to evaluate new therapeutic interventions for genital herpes simplex virus infection. The outcome measured to assess the efficacy of interventions on herpes disease severity is the viral shedding rate, defined as the frequency of detection of HSV on the genital skin and mucosa. We performed a simulation study to ascertain whether our standard model, which we have used previously, was appropriately considering all the necessary features of the shedding data to provide correct inference. We simulated shedding data under our standard, validated assumptions and assessed the ability of 5 different models to reproduce the …
Meta-Analysis Of Gene Expression Studies, Umaporn Siangphoe
Meta-Analysis Of Gene Expression Studies, Umaporn Siangphoe
Theses and Dissertations
Combining effect sizes from individual studies using random-effects models are commonly applied in high-dimensional gene expression data. However, unknown study heterogeneity can arise from inconsistency of sample qualities and experimental conditions. High heterogeneity of effect sizes can reduce statistical power of the models. We proposed two new methods for random effects estimation and measurements for model variation and strength of the study heterogeneity. We then developed a statistical technique to test for significance of random effects and identify heterogeneous genes. We also proposed another meta-analytic approach that incorporates informative weights in the random effects meta-analysis models. We compared the proposed …
Evaluating The Long-Term Effects Of Logging Residue Removals In Great Lakes Aspen Forests, Michael I. Premer
Evaluating The Long-Term Effects Of Logging Residue Removals In Great Lakes Aspen Forests, Michael I. Premer
Dissertations, Master's Theses and Master's Reports
Commercial aspen (Populus spp.) forests of the Great Lakes region are primarily managed for timber products such as pulp fiber and panel board, but logging residues (topwood and non-merchantable bolewood) are potentially important for utilization in the bioenergy market. In some regions, pulp and paper mills already utilize residues as fuel in combustion for heat and electricity, and progressive energy policies will likely cause an increase in biomass feedstock demand. The effects of removing residues, which have a comparatively high concentration of macronutrients, is poorly understood when evaluating long-term site productivity, future timber yields, plant diversity, stand dynamics, and …
Missing At Random And Ignorability For Inferences About Subsets Of Parameters With Missing Data, Roderick J. Little, Sahar Zanganeh
Missing At Random And Ignorability For Inferences About Subsets Of Parameters With Missing Data, Roderick J. Little, Sahar Zanganeh
The University of Michigan Department of Biostatistics Working Paper Series
For likelihood-based inferences from data with missing values, Rubin (1976) showed that the missing data mechanism can be ignored when (a) the missing data are missing at random (MAR), in the sense that missingness does not depend on the missing values after conditioning on the observed data, and (b) the parameters of the data model and the missing-data mechanism are distinct; that is, there are no a priori ties, via parameter space restrictions or prior distributions, between the parameters of the data model and the parameters of the model for the mechanism. Rubin described (a) and (b) as the "weakest …