A New Perspective On Visual Word Processing Efficiency, 2010 Wright State University - Main Campus

#### A New Perspective On Visual Word Processing Efficiency, Joseph W. Houpt, James T. Townsend

*Psychology Faculty Publications*

As a fundamental part of our daily lives, visual word processing has received much attention in the psychological literature. Despite the well established perceptual advantages of word and pseudoword context using accuracy, a comparable effect using response times has been elusive. Some researchers continue to question whether the advantage due to word context is perceptual. We use the capacity coefficient, a well established, response time based measure of efficiency to provide evidence of word processing as a particularly efficient perceptual process to complement those results from the accuracy domain.

A New Perspective On Visual Word Processing Efficiency, 2010 Wright State University - Main Campus

#### A New Perspective On Visual Word Processing Efficiency, Joseph W. Houpt, James T. Townsend

*Joseph W. Houpt*

As a fundamental part of our daily lives, visual word processing has received much attention in the psychological literature. Despite the well established perceptual advantages of word and pseudoword context using accuracy, a comparable effect using response times has been elusive. Some researchers continue to question whether the advantage due to word context is perceptual. We use the capacity coefficient, a well established, response time based measure of efficiency to provide evidence of word processing as a particularly efficient perceptual process to complement those results from the accuracy domain.

Dynamic Model Pooling Methodology For Improving Aberration Detection Algorithms, 2010 University of Massachusetts Amherst

#### Dynamic Model Pooling Methodology For Improving Aberration Detection Algorithms, Brenton J. Sellati

*Masters Theses 1911 - February 2014*

Syndromic surveillance is defined generally as the collection and statistical analysis of data which are believed to be leading indicators for the presence of deleterious activities developing within a system. Conceptually, syndromic surveillance can be applied to any discipline in which it is important to know when external influences manifest themselves in a system by forcing it to depart from its baseline. Comparing syndromic surveillance systems have led to mixed results, where models that dominate in one performance metric are often sorely deficient in another. This results in a zero-sum trade off where one performance metric must be afforded greater ...

Route Choice Behavior In Risky Networks With Real-Time Information, 2010 University of Massachusetts Amherst

#### Route Choice Behavior In Risky Networks With Real-Time Information, Michael D. Razo

*Masters Theses 1911 - February 2014*

This research investigates route choice behavior in networks with risky travel times and real-time information. A stated preference survey is conducted in which subjects use a PC-based interactive maps to choose routes link-by-link in various scenarios. The scenarios include two types of maps: the first presenting a choice between one stochastic route and one deterministic route, and the second with real-time information and an available detour. The first type measures the basic risk attitude of the subject. The second type allows for strategic planning, and measures the effect of this opportunity on subjects' choice behavior.

Results from each subject are ...

Estimation And Prediction Of A Class Of Convolution-Based Spatial Nonstationary Models For Large Spatial Data, 2010 Iowa State University

#### Estimation And Prediction Of A Class Of Convolution-Based Spatial Nonstationary Models For Large Spatial Data, Zhengyuan Zhu, Yichao Wu

*Statistics Publications*

In this article we address two important issues common to the analysis of large spatial datasets. One is the modeling of nonstationarity, and the other is the computational challenges in doing likelihood-based estimation and kriging prediction. We model the spatial process as a convolution of independent Gaussian processes, with the spatially varying kernel function given by the modified Bessel functions. This is a generalization of the process-convolution approach of Higdon, Swall, and Kern (1999), who used the Gaussian kernel to obtain a closed-form nonstationary covariance function. Our model can produce processes with richer local behavior similar to the processes with ...

Probability Models For Blackjack Poker, 2010 Old Dominion University

#### Probability Models For Blackjack Poker, Charlie H. Cooke

*Mathematics & Statistics Faculty Publications*

For simplicity in calculation, previous analyses of blackjack poker have employed models which employ sampling with replacement. in order to assess what degree of error this may induce, the purpose here is to calculate results for a typical hand where sampling without replacement is employed. It is seen that significant error can result when long runs are required to complete the hand. The hand examined is itself of particular interest, as regards both its outstanding expectations of high yield and certain implications for pair splitting of two nines against the dealer's seven. Theoretical and experimental methods are used in ...

Encryption Using Deterministic Chaos, 2010 Technological University Dublin

#### Encryption Using Deterministic Chaos, Jonathan Blackledge, Nikolai Ptitsyn

*Articles*

The concepts of randomness, unpredictability, complexity and entropy form the basis of modern cryptography and a cryptosystem can be interpreted as the design of a key-dependent bijective transformation that is unpredictable to an observer for a given computational resource. For any cryptosystem, including a Pseudo-Random Number Generator (PRNG), encryption algorithm or a key exchange scheme, for example, a cryptanalyst has access to the time series of a dynamic system and knows the PRNG function (the algorithm that is assumed to be based on some iterative process) which is taken to be in the public domain by virtue of the Kerchhoff-Shannon ...

Economic Risk Assessment Using The Fractal Market Hypothesis, 2010 Technological University Dublin

#### Economic Risk Assessment Using The Fractal Market Hypothesis, Jonathan Blackledge, Marek Rebow

*Conference papers*

This paper considers the Fractal Market Hypothesi (FMH) for assessing the risk(s) in developing a financial portfolio based on data that is available through the Internet from an increasing number of sources. Most financial risk management systems are still based on the Efficient Market Hypothesis which often fails due to the inaccuracies of the statistical models that underpin the hypothesis, in particular, that financial data are based on stationary Gaussian processes. The FMH considered in this paper assumes that financial data are non-stationary and statistically self-affine so that a risk analysis can, in principal, be applied at any time ...

Fast Function-On-Scalar Regression With Penalized Basis Expansions, 2009 New York University

#### Fast Function-On-Scalar Regression With Penalized Basis Expansions, Philip T. Reiss, Lei Huang, Maarten Mennes

*Lei Huang*

Regression models for functional responses and scalar predictors are often fitted by means of basis functions, with quadratic roughness penalties applied to avoid overfitting. The fitting approach described by Ramsay and Silverman in the 1990s amounts to a penalized ordinary least squares (P-OLS) estimator of the coefficient functions. We recast this estimator as a generalized ridge regression estimator, and present a penalized generalized least squares (P-GLS) alternative. We describe algorithms by which both estimators can be implemented, with automatic selection of optimal smoothing parameters, in a more computationally efficient manner than has heretofore been available. We discuss pointwise confidence intervals ...

The 1905 Einstein Equation In A General Mathematical Analysis Model Of Quasars, 2009 DePaul University and Columbia College Chicago

#### The 1905 Einstein Equation In A General Mathematical Analysis Model Of Quasars, Byron E. Bell

*Byron E. Bell*

No abstract provided.

Bayesian Inference For A Periodic Stochastic Volatility Model Of Intraday Electricity Prices, 2009 Melbourne Business School

#### Bayesian Inference For A Periodic Stochastic Volatility Model Of Intraday Electricity Prices, Michael S. Smith

*Michael Stanley Smith*

The Gaussian stochastic volatility model is extended to allow for periodic autoregressions (PAR) in both the level and log-volatility process. Each PAR is represented as a first order vector autoregression for a longitudinal vector of length equal to the period. The periodic stochastic volatility model is therefore expressed as a multivariate stochastic volatility model. Bayesian posterior inference is computed using a Markov chain Monte Carlo scheme for the multivariate representation. A circular prior that exploits the periodicity is suggested for the log-variance of the log-volatilities. The approach is applied to estimate a periodic stochastic volatility model for half-hourly electricity prices ...

Bayesian Skew Selection For Multivariate Models, 2009 Melbourne Business School

#### Bayesian Skew Selection For Multivariate Models, Michael S. Smith, Anastasios Panagiotelis

*Michael Stanley Smith*

We develop a Bayesian approach for the selection of skew in multivariate skew t distributions constructed through hidden conditioning in the manners suggested by either Azzalini and Capitanio (2003) or Sahu, Dey and Branco~(2003). We show that the skew coefficients for each margin are the same for the standardized versions of both distributions. We introduce binary indicators to denote whether there is symmetry, or skew, in each dimension. We adopt a proper beta prior on each non-zero skew coefficient, and derive the corresponding prior on the skew parameters. In both distributions we show that as the degrees of freedom ...

Fast Function-On-Scalar Regression With Penalized Basis Expansions, 2009 New York University

#### Fast Function-On-Scalar Regression With Penalized Basis Expansions, Philip T. Reiss, Lei Huang, Maarten Mennes

*Philip T. Reiss*

Regression models for functional responses and scalar predictors are often fitted by means of basis functions, with quadratic roughness penalties applied to avoid overfitting. The fitting approach described by Ramsay and Silverman in the 1990s amounts to a penalized ordinary least squares (P-OLS) estimator of the coefficient functions. We recast this estimator as a generalized ridge regression estimator, and present a penalized generalized least squares (P-GLS) alternative. We describe algorithms by which both estimators can be implemented, with automatic selection of optimal smoothing parameters, in a more computationally efficient manner than has heretofore been available. We discuss pointwise confidence intervals ...