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1078 full-text articles. Page 1 of 27.

Mechanistic Mathematical Models: An Underused Platform For Hpv Research, Marc Ryser, Patti Gravitt, Evan R. Myers 2017 George Washington University

Mechanistic Mathematical Models: An Underused Platform For Hpv Research, Marc Ryser, Patti Gravitt, Evan R. Myers

Global Health Faculty Publications

Health economic modeling has become an invaluable methodology for the design and evaluation of clinical and public health interventions against the human papillomavirus (HPV) and associated diseases. At the same time, relatively little attention has been paid to a different yet complementary class of models, namely that of mechanistic mathematical models. The primary focus of mechanistic mathematical models is to better understand the intricate biologic mechanisms and dynamics of disease. Inspired by a long and successful history of mechanistic modeling in other biomedical fields, we highlight several areas of HPV research where mechanistic models have the potential to advance the ...


Implementing Propensity Score Matching With Network Data: The Effect Of Gatt On Bilateral Trade, Luca De Benedictis, Bruno Arpino, Alessandra Mattei 2017 Universitat Pompeu Fabra

Implementing Propensity Score Matching With Network Data: The Effect Of Gatt On Bilateral Trade, Luca De Benedictis, Bruno Arpino, Alessandra Mattei

Luca De Benedictis

Motivated by the evaluation of the causal effect of the General Agreement on Tariffs and Trade on bilateral international trade flows, we investigate the role of network structure in propensity score matching under the assumption of strong ignorability. We study the sensitivity of causal inference with respect to the presence of characteristics of the network in the set of confounders conditional on which strong ignorability is assumed to hold. We find that estimates of the average causal effect are highly sensitive to the presence of node-level network statistics in the set of confounders. Therefore, we argue that estimates may suffer ...


Massively Parallel Approximate Gaussian Process Regression, Robert B. Gramacy, Jarad Niemi, Robin M. Weiss 2017 University of Chicago

Massively Parallel Approximate Gaussian Process Regression, Robert B. Gramacy, Jarad Niemi, Robin M. Weiss

Jarad Niemi

We explore how the big-three computing paradigms---symmetric multiprocessor, graphical processing units (GPUs), and cluster computing---can together be brought to bear on large-data Gaussian processes (GP) regression problems via a careful implementation of a newly developed local approximation scheme. Our methodological contribution focuses primarily on GPU computation, as this requires the most care and also provides the largest performance boost. However, in our empirical work we study the relative merits of all three paradigms to determine how best to combine them. The paper concludes with two case studies. One is a real data fluid-dynamics computer experiment which benefits from the local ...


Empirical Bayes Analysis Of Rna-Seq Data For Detection Of Gene Expression Heterosis, Jarad Niemi, Eric Mittman, Will Landau, Dan Nettleton 2017 Iowa State University

Empirical Bayes Analysis Of Rna-Seq Data For Detection Of Gene Expression Heterosis, Jarad Niemi, Eric Mittman, Will Landau, Dan Nettleton

Jarad Niemi

An important type of heterosis, known as hybrid vigor, refers to the enhancements in the phenotype of hybrid progeny relative to their inbred parents. Although hybrid vigor is extensively utilized in agriculture, its molecular basis is still largely unknown. In an effort to understand phenotypic heterosis at the molecular level, researchers are measuring transcript abundance levels of thousands of genes in parental inbred lines and their hybrid offspring using RNA sequencing (RNA-seq) technology. The resulting data allow researchers to search for evidence of gene expression heterosis as one potential molecular mechanism underlying heterosis of agriculturally important traits. The null hypotheses ...


Interweaving Markov Chain Monte Carlo Strategies For Efficient Estimation Of Dynamic Linear Models, Matthew Simpson, Jarad Niemi, Vivekananda Roy 2017 University of Missouri

Interweaving Markov Chain Monte Carlo Strategies For Efficient Estimation Of Dynamic Linear Models, Matthew Simpson, Jarad Niemi, Vivekananda Roy

Jarad Niemi

In dynamic linear models (DLMs) with unknown fixed parameters, a standard Markov chain Monte Carlo (MCMC) sampling strategy is to alternate sampling of latent states conditional on fixed parameters and sampling of fixed parameters conditional on latent states. In some regions of the parameter space, this standard data augmentation (DA) algorithm can be inefficient. To improve efficiency, we apply the interweaving strategies of Yu and Meng to DLMs. For this, we introduce three novel alternative DAs for DLMs: the scaled errors, wrongly scaled errors, and wrongly scaled disturbances. With the latent states and the less well known scaled disturbances, this ...


Estimation And Prediction In Spatial Models With Block Composite Likelihoods, Jo Eidsvik, Benjamin A. Shaby, Brian J. Reich, Matthew Wheeler, Jarad Niemi 2017 University of Trondeim

Estimation And Prediction In Spatial Models With Block Composite Likelihoods, Jo Eidsvik, Benjamin A. Shaby, Brian J. Reich, Matthew Wheeler, Jarad Niemi

Jarad Niemi

This article develops a block composite likelihood for estimation and prediction in large spatial datasets. The composite likelihood (CL) is constructed from the joint densities of pairs of adjacent spatial blocks. This allows large datasets to be split into many smaller datasets, each of which can be evaluated separately, and combined through a simple summation. Estimates for unknown parameters are obtained by maximizing the block CL function. In addition, a new method for optimal spatial prediction under the block CL is presented. Asymptotic variances for both parameter estimates and predictions are computed using Godambe sandwich matrices. The approach considerably improves ...


Phylogenies, The Comparative Method, And The Conflation Of Tempo And Mode, Antigoni Kaliontzopoulou, Dean C. Adams 2017 CIBIO/InBio, Vairão, Portugal

Phylogenies, The Comparative Method, And The Conflation Of Tempo And Mode, Antigoni Kaliontzopoulou, Dean C. Adams

Dean C. Adams

The comparison of mathematical models that represent alternative hypotheses about the tempo and mode of evolutionary change is a common approach for assessing the evolutionary processes underlying phenotypic diversification. However, because model parameters are estimated simultaneously, they are inextricably linked, such that changes in tempo, the pace of evolution, and mode, the manner in which evolution occurs, may be difficult to assess separately. This may potentially complicate biological interpretation, but the extent to which this occurs has not yet been determined. In this study, we examined 160 phylogeny × trait empirical datasets, and conducted extensive numerical phylogenetic simulations, to investigate the ...


Interweaving Markov Chain Monte Carlo Strategies For Efficient Estimation Of Dynamic Linear Models, Matthew Simpson, Jarad Niemi, Vivekananda Roy 2017 University of Missouri

Interweaving Markov Chain Monte Carlo Strategies For Efficient Estimation Of Dynamic Linear Models, Matthew Simpson, Jarad Niemi, Vivekananda Roy

Statistics Publications

In dynamic linear models (DLMs) with unknown fixed parameters, a standard Markov chain Monte Carlo (MCMC) sampling strategy is to alternate sampling of latent states conditional on fixed parameters and sampling of fixed parameters conditional on latent states. In some regions of the parameter space, this standard data augmentation (DA) algorithm can be inefficient. To improve efficiency, we apply the interweaving strategies of Yu and Meng to DLMs. For this, we introduce three novel alternative DAs for DLMs: the scaled errors, wrongly scaled errors, and wrongly scaled disturbances. With the latent states and the less well known scaled disturbances, this ...


The Battle Against Malaria: A Teachable Moment, Randy K. Schwartz 2017 Schoolcraft College

The Battle Against Malaria: A Teachable Moment, Randy K. Schwartz

Journal of Humanistic Mathematics

Malaria has been humanity’s worst public health problem throughout recorded history. Mathematical methods are needed to understand which factors are relevant to the disease and to develop counter-measures against it. This article and the accompanying exercises provide examples of those methods for use in lower- or upper-level courses dealing with probability, statistics, or population modeling. These can be used to illustrate such concepts as correlation, causation, conditional probability, and independence. The article explains how the apparent link between sickle cell trait and resistance to malaria was first verified in Uganda using the chi-squared probability distribution. It goes on to ...


Data Predictive Control For Building Energy Management, Achin Jain, Madhur Behl, Rahul Mangharam 2017 University of Pennsylvania

Data Predictive Control For Building Energy Management, Achin Jain, Madhur Behl, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

Decisions on how to best optimize energy systems operations are becoming ever so complex and conflicting, that model-based predictive control (MPC) algorithms must play an important role. However, a key factor prohibiting the widespread adoption of MPC in buildings, is the cost, time, and effort associated with learning first-principles based dynamical models of the underlying physical system. This paper introduces an alternative approach for implementing finite-time receding horizon control using control-oriented data-driven models. We call this approach Data Predictive Control (DPC). Specifically, by utilizing separation of variables, two novel algorithms for implementing DPC using a single regression tree and with ...


The Spatial Dimensions Of State Fiscal Capacity The Mechanisms Of International Influence On Domestic Extractive Efforts, Cameron G. Thies, Olga Chyzh, Mark David Nieman 2017 Arizona State University

The Spatial Dimensions Of State Fiscal Capacity The Mechanisms Of International Influence On Domestic Extractive Efforts, Cameron G. Thies, Olga Chyzh, Mark David Nieman

Mark David Nieman

This paper expands traditional predatory theory approaches to state fiscal capacity by adopting spatial analytical reasoning and methods. While previous work in the predatory theory tradition has often incorporated interdependent external influences, such as war and trade, it has often done so in a way that maintains a theoretical and empirical autonomy of the state. Theoretically, we suggest four mechanisms (coercion, competition, learning, and emulation) that operate to channel information through interstate rivalry and territorial contiguity, trade networks, and the political space associated with regime type and intergovernmental organization membership. We test our predictions using a multi-parametric spatio-temporal autoregressive model ...


Inference In Networking Systems With Designed Measurements, Chang Liu 2017 University of Massachusetts - Amherst

Inference In Networking Systems With Designed Measurements, Chang Liu

Doctoral Dissertations May 2014 - current

Networking systems consist of network infrastructures and the end-hosts have been essential in supporting our daily communication, delivering huge amount of content and large number of services, and providing large scale distributed computing. To monitor and optimize the performance of such networking systems, or to provide flexible functionalities for the applications running on top of them, it is important to know the internal metrics of the networking systems such as link loss rates or path delays. The internal metrics are often not directly available due to the scale and complexity of the networking systems. This motivates the techniques of inference ...


Pointwise Influence Matrices For Functional-Response Regression, Philip T. Reiss, Lei Huang, Pei-Shien Wu, Huaihou Chen, Stan Colcombe 2016 New York University School of Medicine

Pointwise Influence Matrices For Functional-Response Regression, Philip T. Reiss, Lei Huang, Pei-Shien Wu, Huaihou Chen, Stan Colcombe

Philip T. Reiss

We extend the notion of an influence or hat matrix to regression with functional responses and scalar predictors. For responses depending linearly on a set of predictors, our definition is shown to reduce to the conventional influence matrix for linear models. The pointwise degrees of freedom, the trace of the pointwise hat matrix, are shown to have an adaptivity property that motivates a two-step bivariate smoother for modeling nonlinear dependence on a single predictor. This procedure adapts to varying complexity of the nonlinear model at different locations along the function, and thereby achieves better performance than competing tensor product smoothers ...


Penalized Nonparametric Scalar-On-Function Regression Via Principal Coordinates, Philip T. Reiss, David L. Miller, Pei-Shien Wu, Wen-Yu Hua 2016 New York University School of Medicine

Penalized Nonparametric Scalar-On-Function Regression Via Principal Coordinates, Philip T. Reiss, David L. Miller, Pei-Shien Wu, Wen-Yu Hua

Philip T. Reiss

A number of classical approaches to nonparametric regression have recently been extended to the case of functional predictors. This paper introduces a new method of this type, which extends intermediate-rank penalized smoothing to scalar-on-function regression. The core idea is to regress the response on leading principal coordinates defined by a relevant distance among the functional predictors, while applying a ridge penalty. Our publicly available implementation, based on generalized additive modeling software, allows for fast optimal tuning parameter selection and for extensions to multiple functional predictors, exponential family-valued responses, and mixed-effects models. In an application to signature verification data, the proposed ...


Novel Models Of Visual Topographic Map Alignment In The Superior Colliculus., Ruben A Tikidji-Hamburyan, Tarek A El-Ghazawi, Jason W. Triplett 2016 George Washington University

Novel Models Of Visual Topographic Map Alignment In The Superior Colliculus., Ruben A Tikidji-Hamburyan, Tarek A El-Ghazawi, Jason W. Triplett

Pediatrics Faculty Publications

The establishment of precise neuronal connectivity during development is critical for sensing the external environment and informing appropriate behavioral responses. In the visual system, many connections are organized topographically, which preserves the spatial order of the visual scene. The superior colliculus (SC) is a midbrain nucleus that integrates visual inputs from the retina and primary visual cortex (V1) to regulate goal-directed eye movements. In the SC, topographically organized inputs from the retina and V1 must be aligned to facilitate integration. Previously, we showed that retinal input instructs the alignment of V1 inputs in the SC in a manner dependent on ...


Monte Carlo Simulation In Environmental Risk Assessment--Science, Policy And Legal Issues, Susan R. Poulter 2016 University of New Hampshire

Monte Carlo Simulation In Environmental Risk Assessment--Science, Policy And Legal Issues, Susan R. Poulter

RISK: Health, Safety & Environment

Dr. Poulter notes that agencies should anticipate judicial requirements for justification of Monte Carlo simulations and, meanwhile, should consider, e.g., whether their use will make risk assessment policy choices more opaque or apparent.


A Multi-Indexed Logistic Model For Time Series, Xiang Liu 2016 East Tennessee State University

A Multi-Indexed Logistic Model For Time Series, Xiang Liu

Electronic Theses and Dissertations

In this thesis, we explore a multi-indexed logistic regression (MILR) model, with particular emphasis given to its application to time series. MILR includes simple logistic regression (SLR) as a special case, and the hope is that it will in some instances also produce significantly better results. To motivate the development of MILR, we consider its application to the analysis of both simulated sine wave data and stock data. We looked at well-studied SLR and its application in the analysis of time series data. Using a more sophisticated representation of sequential data, we then detail the implementation of MILR. We compare ...


A Traders Guide To The Predictive Universe- A Model For Predicting Oil Price Targets And Trading On Them, Jimmie Harold Lenz 2016 Washington University in St. Louis

A Traders Guide To The Predictive Universe- A Model For Predicting Oil Price Targets And Trading On Them, Jimmie Harold Lenz

Doctor of Business Administration Dissertations

At heart every trader loves volatility; this is where return on investment comes from, this is what drives the proverbial “positive alpha.” As a trader, understanding the probabilities related to the volatility of prices is key, however if you could also predict future prices with reliability the world would be your oyster. To this end, I have achieved three goals with this dissertation, to develop a model to predict future short term prices (direction and magnitude), to effectively test this by generating consistent profits utilizing a trading model developed for this purpose, and to write a paper that anyone with ...


Data Predictive Control For Peak Power Reduction, Achin Jain, Madhur Behl, Rahul Mangharam 2016 University of Pennsylvania

Data Predictive Control For Peak Power Reduction, Achin Jain, Madhur Behl, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

Decisions on how best to optimize today's energy systems operations are becoming ever so complex and conflicting such that model-based predictive control algorithms must play a key role. However, learning dynamical models of energy consuming systems such as buildings, using grey/white box approaches is very cost and time prohibitive due to its complexity. This paper presents data-driven methods for making control-oriented model for peak power reduction in buildings. Specifically, a data predictive control with regression trees (DPCRT) algorithm, is presented. DPCRT is a finite receding horizon method, using which the building operator can optimally trade off peak power ...


Towards Deeper Understanding In Neuroimaging, Rex Devon Hjelm 2016 Mind Research Network

Towards Deeper Understanding In Neuroimaging, Rex Devon Hjelm

Computer Science ETDs

Neuroimaging is a growing domain of research, with advances in machine learning having tremendous potential to expand understanding in neuroscience and improve public health. Deep neural networks have recently and rapidly achieved historic success in numerous domains, and as a consequence have completely redefined the landscape of automated learners, giving promise of significant advances in numerous domains of research. Despite recent advances and advantages over traditional machine learning methods, deep neural networks have yet to have permeated significantly into neuroscience studies, particularly as a tool for discovery. This dissertation presents well-established and novel tools for unsupervised learning which aid in ...


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