Open Access. Powered by Scholars. Published by Universities.®

Statistics and Probability Commons

Open Access. Powered by Scholars. Published by Universities.®

11,206 Full-Text Articles 16,471 Authors 3,366,482 Downloads 227 Institutions

All Articles in Statistics and Probability

Faceted Search

11,206 full-text articles. Page 1 of 330.

Boundary Data Maps For Schrödinger Operators On A Compact Interval, Stephen L. Clark, Fritz Gesztesy, M. Mitrea 2019 Missouri University of Science and Technology

Boundary Data Maps For Schrödinger Operators On A Compact Interval, Stephen L. Clark, Fritz Gesztesy, M. Mitrea

Stephen L. Clark

We provide a systematic study of boundary data maps, that is, 2 x 2 matrix-valued Dirichlet-to-Neumann and more generally, Robin-to-Robin maps, associated with one-dimensional Schrödinger operators on a compact interval [0, R] with separated boundary conditions at 0 and R. Most of our results are formulated in the non-self-adjoint context. Our principal results include explicit representations of these boundary data maps in terms of the resolvent of the underlying Schrödinger operator and the associated boundary trace maps, Krein-type resolvent formulas relating Schrödinger operators corresponding to different (separated) boundary conditions, and a derivation of the Herglotz property of boundary data maps ...


Forward Selection Via Distance Correlation, Ty Adams 2019 Rose-Hulman Institute of Technology

Forward Selection Via Distance Correlation, Ty Adams

Mathematical Sciences Technical Reports (MSTR)

No abstract provided.


Advances In Measurement Error Modeling, Linh Nghiem 2019 Southern Methodist University

Advances In Measurement Error Modeling, Linh Nghiem

Statistical Science Theses and Dissertations

Measurement error in observations is widely known to cause bias and a loss of power when fitting statistical models, particularly when studying distribution shape or the relationship between an outcome and a variable of interest. Most existing correction methods in the literature require strong assumptions about the distribution of the measurement error, or rely on ancillary data which is not always available. This limits the applicability of these methods in many situations. Furthermore, new correction approaches are also needed for high-dimensional settings, where the presence of measurement error in the covariates adds another level of complexity to the desirable structure ...


Samples, Unite! Understanding The Effects Of Matching Errors On Estimation Of Total When Combining Data Sources, Benjamin Williams 2019 Southern Methodist University

Samples, Unite! Understanding The Effects Of Matching Errors On Estimation Of Total When Combining Data Sources, Benjamin Williams

Statistical Science Theses and Dissertations

Much recent research has focused on methods for combining a probability sample with a non-probability sample to improve estimation by making use of information from both sources. If units exist in both samples, it becomes necessary to link the information from the two samples for these units. Record linkage is a technique to link records from two lists that refer to the same unit but lack a unique identifier across both lists. Record linkage assigns a probability to each potential pair of records from the lists so that principled matching decisions can be made. Because record linkage is a probabilistic ...


Characterizing The Permanence And Stationary Distribution For A Family Of Malaria Stochastic Models, Divine Wanduku 2019 Virginia Commonwealth University

Characterizing The Permanence And Stationary Distribution For A Family Of Malaria Stochastic Models, Divine Wanduku

Biology and Medicine Through Mathematics Conference

No abstract provided.


Variational Inference For Quantile Rgression, Bufei Guo 2019 Washington University in St. Louis

Variational Inference For Quantile Rgression, Bufei Guo

Arts & Sciences Electronic Theses and Dissertations

Quantile regression (QR) (Koenker and Bassett, 1978), is an alternative to classic lin- ear regression with extensive applications in many fields. This thesis studies Bayesian quantile regression (Yu and Moyeed, 2001) using variational inference, which is one of the alternative methods to the Markov chain Monte Carlo (MCMC) in approximating intractable posterior distributions. The lasso regularization is shown to be effective in improving the accuracy of quantile regression (Li and Zhu, 2008). This thesis developed variational inference for quantile regression and regularized quantile regression with the lasso penalty. Simulation results show that variational inference is a computationally more efficient alternative ...


Measuring Clinical Weight Loss In Young Children With Severe Obesity: Comparison Of Outcomes Using Zbmi, Modified Zbmi, And Percent Of 95th Percentile, Carolyn Bates 2019 Children's Mercy Kansas City

Measuring Clinical Weight Loss In Young Children With Severe Obesity: Comparison Of Outcomes Using Zbmi, Modified Zbmi, And Percent Of 95th Percentile, Carolyn Bates

Research Days

No abstract provided.


Selecting Maximally-Predictive Deep Features To Explain What Drives Fixations In Free-Viewing, Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge 2019 University of Tübingen

Selecting Maximally-Predictive Deep Features To Explain What Drives Fixations In Free-Viewing, Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge

MODVIS Workshop

No abstract provided.


Predictive Performance Of Existing Population Pharmacokinetic Models Of Tacrolimus In Pediatric Kidney Transplant Recipients, Alenka Chapron 2019 Children's Mercy Hospital, Kansas City, MO

Predictive Performance Of Existing Population Pharmacokinetic Models Of Tacrolimus In Pediatric Kidney Transplant Recipients, Alenka Chapron

Research Days

No abstract provided.


Quantifying Sleep Architecture For Pediatric Hypersomnia Conditions, Alicia K. Colclasure 2019 Colorado School of Mines

Quantifying Sleep Architecture For Pediatric Hypersomnia Conditions, Alicia K. Colclasure

Biology and Medicine Through Mathematics Conference

No abstract provided.


Prospective Evaluation Of A Population Pharmacokinetic Model Of Pantoprazole For Obese Children, Alenka Chapron 2019 Children's Mercy Hospital, Kansas City, MO

Prospective Evaluation Of A Population Pharmacokinetic Model Of Pantoprazole For Obese Children, Alenka Chapron

Research Days

No abstract provided.


Self-Driving Cars: Evaluation Of Deep Learning Techniques For Object Detection In Different Driving Conditions, Ramesh Simhambhatla, Kevin Okiah, Shravan Kuchkula, Robert Slater 2019 SMU

Self-Driving Cars: Evaluation Of Deep Learning Techniques For Object Detection In Different Driving Conditions, Ramesh Simhambhatla, Kevin Okiah, Shravan Kuchkula, Robert Slater

SMU Data Science Review

Deep Learning has revolutionized Computer Vision, and it is the core technology behind capabilities of a self-driving car. Convolutional Neural Networks (CNNs) are at the heart of this deep learning revolution for improving the task of object detection. A number of successful object detection systems have been proposed in recent years that are based on CNNs. In this paper, an empirical evaluation of three recent meta-architectures: SSD (Single Shot multi-box Detector), R-CNN (Region-based CNN) and R-FCN (Region-based Fully Convolutional Networks) was conducted to measure how fast and accurate they are in identifying objects on the road, such as vehicles, pedestrians ...


Blacklegged Tick (Ixodes Scapularis) Distribution In Maine, Usa, As Related To Climate Change, White-Tailed Deer, And The Landscape, Susan P. Elias 2019 University of Maine

Blacklegged Tick (Ixodes Scapularis) Distribution In Maine, Usa, As Related To Climate Change, White-Tailed Deer, And The Landscape, Susan P. Elias

Electronic Theses and Dissertations

Lyme disease is caused by the bacterial spirochete Borrelia burgdorferi, which is transmitted through the bite of an infected blacklegged (deer) tick (Ixodes scapularis). Geographic invasion of I. scapularis in North America has been attributed to causes including 20th century reforestation and suburbanization, burgeoning populations of the white-tailed deer (Odocoileus virginianus) which is the primary reproductive host of I. scapularis, tick-associated non-native plant invasions, and climate change. Maine, USA, is a high Lyme disease incidence state, with a history of increasing I. scapularis abundance and northward range expansion. This thesis addresses the question: “To what extent has the range expansion ...


Tik-Means: Transformation-Infused K-Means Clustering For Skewed Groups, Nicholas S. Berry, Ranjan Maitra 2019 Iowa State University

Tik-Means: Transformation-Infused K-Means Clustering For Skewed Groups, Nicholas S. Berry, Ranjan Maitra

Ranjan Maitra

The K-means algorithm is extended to allow for partitioning of skewed groups. Our algorithm is called TiK-Means and contributes a K-means type algorithm that assigns observations to groups while estimating their skewness-transformation parameters. The resulting groups and transformation reveal general-structured clusters that can be explained by inverting the estimated transformation. Further, a modification of the jump statistic chooses the number of groups. Our algorithm is evaluated on simulated and real-life datasets and then applied to a long-standing astronomical dispute regarding the distinct kinds of gamma ray bursts.


Fast Spatial Inference In The Homogeneous Ising Model, Alejandro Murua, Ranjan Maitra 2019 Universite de Montreal

Fast Spatial Inference In The Homogeneous Ising Model, Alejandro Murua, Ranjan Maitra

Ranjan Maitra

The Ising model is important in statistical modeling and inference in many applications, however its normalizing constant, mean number of active vertices and mean spin interaction are intractable. We provide accurate approximations that make it possible to calculate these quantities numerically. Simulation studies indicate good performance when compared to Markov Chain Monte Carlo methods and at a tiny fraction of the time. The methodology is also used to perform Bayesian inference in a functional Magnetic Resonance Imaging activation detection experiment.


Kernel-Estimated Nonparametric Overlap-Based Syncytial Clustering, Israel Almodóvar-Rivera, Ranjan Maitra 2019 University of Puerto Rico - Medical Sciences Campus

Kernel-Estimated Nonparametric Overlap-Based Syncytial Clustering, Israel Almodóvar-Rivera, Ranjan Maitra

Ranjan Maitra

Standard clustering algorithms usually find regular-structured clusters such as ellipsoidally- or spherically-dispersed groups, but are more challenged with groups lacking formal structure or definition. Syncytial clustering is the name that we introduce for methods that merge groups obtained from standard clustering algorithms in order to reveal complex group structure in the data. Here, we develop a distribution-free fully-automated syncytial clustering algorithm that can be used with k-means and other algorithms. Our approach computes the cumulative distribution function of the normed residuals from an appropriately fit k-groups model and calculates the nonparametric overlap between each pair of groups. Groups with high ...


Fast Adaptive Smoothing And Thresholding For Improved Activation Detection In Low-Signal Fmri, Israel Almodovar-Rivera, Ranjan Maitra 2019 University of Puerto Rico

Fast Adaptive Smoothing And Thresholding For Improved Activation Detection In Low-Signal Fmri, Israel Almodovar-Rivera, Ranjan Maitra

Ranjan Maitra

Functional Magnetic Resonance Imaging is a noninvasive tool used to study brain function. Detecting activation is challenged by many factors, and even more so in low-signal scenarios that arise in the performance of high-level cognitive tasks. We provide a fully automated and fast adaptive smoothing and thresholding (FAST) algorithm that uses smoothing and extreme value theory on correlated statistical parametric maps for thresholding. Performance on experiments spanning a range of low-signal settings is very encouraging. The methodology also performs well in a study to identify the cerebral regions that perceive only-auditory-reliable and only-visual-reliable speech stimuli as well as those that ...


Assessing Significance In Finite Mixture Models, Ranjan Maitra, Volodymyr Melnykov 2019 Iowa State University

Assessing Significance In Finite Mixture Models, Ranjan Maitra, Volodymyr Melnykov

Ranjan Maitra

A new method is proposed to quantify significance in finite mixture models. The basis for this new methodology is an approach that calculates the p-value for testing a simpler model against a more complicated one in a way that is able to obviate the failure of regularity conditions for likelihood ratio tests. The developed testing procedure allows for pairwise comparison of any two mixture models with failure to reject the null hypothesis implying insignificant likelihood improvement under the more complex model. This leads to a comprehensive tool called a quantitation map which displays significance and quantitatively summarizes all model comparisons ...


Efficient Bandwidth Estimation In Two-Dimensional Filtered Backprojection Reconstruction, Ranjan Maitra 2019 Iowa State University

Efficient Bandwidth Estimation In Two-Dimensional Filtered Backprojection Reconstruction, Ranjan Maitra

Ranjan Maitra

A generalized cross-validation approach to estimate the reconstruction filter bandwidth in two-dimensional Filtered Backprojection is presented. The method writes the reconstruction equation in equivalent backprojected filtering form, derives results on eigendecomposition of symmetric two-dimensional circulant matrices and applies them to make bandwidth estimation a computationally efficient operation within the context of standard backprojected filtering reconstruction. Performance evaluations on a wide range of simulated emission tomography experiments give promising results. The superior performance holds at both low and high total expected counts, pointing to the method's applicability even in weaker signal-noise situations. The approach also applies to the more general ...


Three-Dimensional Radial Visualization Of High-Dimensional Continuous Or Discrete Data, Fan Dai, Yifan Zhu, Ranjan Maitra 2019 Iowa State University

Three-Dimensional Radial Visualization Of High-Dimensional Continuous Or Discrete Data, Fan Dai, Yifan Zhu, Ranjan Maitra

Ranjan Maitra

This paper develops methodology for 3D radial visualization of high-dimensional datasets. Our display engine is called RadViz3D and extends the classic RadViz that visualizes multivariate data in the 2D plane by mapping every record to a point inside the unit circle. The classic RadViz display has equally-spaced anchor points on the unit circle, with each of them associated with an attribute or feature of the dataset. RadViz3D obtains equi-spaced anchor points exactly for the five Platonic solids and approximately for the other cases via a Fibonacci grid. We show that distributing anchor points at least approximately uniformly on the 3D ...


Digital Commons powered by bepress