Outreach Program to Develop And Implement Local Land Use Regulations to Protect the Remaining Undisturbed Natural Shoreland Buffers in the Towns of Candia and Deerfield, NH

The towns of Candia and Deerfield, New Hampshire, both situated within the Great Bay/Little Bay watershed and the Lamprey River subwatershed have agreed to participate with the Southern New Hampshire Regional Planning Commission (SNHPC) to develop and implement land use regulations to protect the remaining undisturbed natural shoreline buffers along the Lamprey and North Branch Rivers (2nd order or higher streams and tributaries) and other surface waters within these communities

The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions

Discovering interaction effects on a response of interest is a fundamental problem faced in biology, medicine, economics, and many other scientific disciplines. In theory, Bayesian methods for discovering pairwise interactions enjoy many benefits such as coherent uncertainty quantification, the ability to incorporate background knowledge, and desirable shrinkage properties. In practice, however, Bayesian methods are often computationally intractable for even moderate-dimensional problems. Our key insight is that many hierarchical models of practical interest admit a particular Gaussian process (GP) representation; the GP allows us to capture the posterior with a vector of O(p) kernel hyper-parameters rather than O(p^2) interactions and main effects. With the implicit representation, we can run Markov chain Monte Carlo (MCMC) over model hyper-parameters in time and memory linear in p per iteration. We focus on sparsity-inducing models and show on datasets with a variety of covariate behaviors that our method: (1) reduces runtime by orders of magnitude over naive applications of MCMC, (2) provides lower Type I and Type II error relative to state-of-the-art LASSO-based approaches, and (3) offers improved computational scaling in high dimensions relative to existing Bayesian and LASSO-based approaches.Comment: Accepted at ICML 2019. 20 pages, 4 figures, 3 table

The RadFxSat-2 Mission to Measure SEU Rates in FinFET Microelectronics

The RadFxSat-2 mission was launched January 17, 2021 with Virgin Orbit\u27s LauncherOne under the NASA ELaNa-20 initiative. RadFxSat-2 carries a radiation effects payload designed to investigate single event upsets (SEUs) in sub-65 nm commercial memories, including a FinFET-based memory. Sub-65 nm technologies have demonstrated enhanced sensitivity to low-energy protons, but current models have not considered low-energy protons as a source of SEUs. Missions utilizing the latest commercial technologies could experience a higher error rate than predicted. RadFxSat-2 was designed to assess SEU rates for FinFET SRAMs operated in low-Earth orbit (LEO), a proton-heavy environment. Details of the mission and data collected over the previous two years are presented. Results from RadFxSat-2 suggest that FinFET-based microelectronic technologies are suitable for high-performance, high-density storage in LEO

Canagliflozin and renal outcomes in type 2 diabetes and nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to <90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], >300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years

Bayesian Linear Modeling in High Dimensions: Advances in Hierarchical Modeling, Inference, and Evaluation

Across the sciences, social sciences and engineering, applied statisticians seek to build understandings of complex relationships from increasingly large datasets. In statistical genetics, for example, we observe up to millions of genetic variations in each of thousands of individuals, and wish to associate these variations with the development of disease. For ‘high dimensional’ problems like this, the languages of linear modeling and Bayesian statistics appeal because they provide interpretability, coherent uncertainty, and the capacity for information sharing across related datasets. But at the same time, high dimensionality introduces several challenges not solved by existing methodology. This thesis addresses three challenges that arise when applying the Bayesian methodology in high dimensions. A first challenge is how to apply hierarchical modeling, a mainstay of Bayesian inference, to share information between multiple linear models with many covariates (for example, genetic studies of multiple related diseases). The first part of the thesis demonstrates that the default approach to hierarchical linear modeling fails in high dimensions, and presents a new, effective model for this regime. The second part of the thesis addresses the computational challenge presented by Bayesian inference in high dimensions — existing methods demand time that scales super-linearly with the number of covariates. We present two algorithms that permit fast, accurate inferences by leveraging (i) low rank approximations of data or (ii) parallelism across a certain class of Markov chain Monte Carlo algorithms. The final part of the thesis addresses the challenge of evaluation. Modern statistics provides an expansive toolkit for estimating unknown parameters, and a typical Bayesian analysis justifies its estimates through belief in subjective a priori assumptions. We address this by introducing a measure of confidence in the new estimate (the ‘c-value’), that can diagnose the accuracy of a Bayesian estimate without requiring this subjectivism.Ph.D

LR-GLM: High-dimensional Bayesian inference using low-rank data approximations

Due to the ease of modern data collection, applied statisticians often have access to a large set of covariates that they wish to relate to some observed outcome. Generalized linear models (GLMs) offer a particularly interpretable framework for such an analysis. In these high-dimensional problems, the number of covariates is often large relative to the number of observations, so we face non-trivial inferential uncertainty; a Bayesian approach allows coherent quantification of this uncertainty. Unfortunately, existing methods for Bayesian inference in GLMs require running times roughly cubic in parameter dimension, and so are limited to settings with at most tens of thousand parameters. We propose to reduce time and memory costs with a low-rank approximation of the data in an approach we call LR-GLM. When used with the Laplace approximation or Markov chain Monte Carlo, LR-GLM provides a full Bayesian posterior approximation and admits running times reduced by a full factor of the parameter dimension. We rigorously establish the quality of our approximation and show how the choice of rank allows a tunable computational-statistical trade-off. Experiments support our theory and demonstrate the efficacy of LR-GLM on real large-scale datasets

Confidently Comparing Estimates with the c-value

Modern statistics provides an ever-expanding toolkit for estimating unknown parameters. Consequently, applied statisticians frequently face a difficult decision: retain a parameter estimate from a familiar method or replace it with an estimate from a newer or more complex one. While it is traditional to compare estimates using risk, such comparisons are rarely conclusive in realistic settings. In response, we propose the “c-value” as a measure of confidence that a new estimate achieves smaller loss than an old estimate on a given dataset. We show that it is unlikely that a large c-value coincides with a larger loss for the new estimate. Therefore, just as a small p-value supports rejecting a null hypothesis, a large c-value supports using a new estimate in place of the old. For a wide class of problems and estimates, we show how to compute a c-value by first constructing a data-dependent high-probability lower bound on the difference in loss. The c-value is frequentist in nature, but we show that it can provide validation of shrinkage estimates derived from Bayesian models in real data applications involving hierarchical models and Gaussian processes.</p