Investigation of spatial misregistration effects in multispectral scanner data

The author has identified the following significant results. A model for estimating the expected proportion of multiclass pixels in a scene was generalized and extended to include misregistration effects. Another substantial effort was the development of a simulation model to generate signatures to represent the distributions of signals from misregistered multiclass pixels, based on single class signatures. Spatial misregistration causes an increase in the proportion of multiclass pixels in a scene and a decorrelation between signals in misregistered data channels. The multiclass pixel proportion estimation model indicated that this proportion is strongly dependent on the pixel perimeter and on the ratio of the total perimeter of the fields in the scene to the area of the scene. Test results indicated that expected values computed with this model were similar to empirical measurements made of this proportion in four LACIE data segments

Multiple testing procedures under confounding

While multiple testing procedures have been the focus of much statistical research, an important facet of the problem is how to deal with possible confounding. Procedures have been developed by authors in genetics and statistics. In this chapter, we relate these proposals. We propose two new multiple testing approaches within this framework. The first combines sensitivity analysis methods with false discovery rate estimation procedures. The second involves construction of shrinkage estimators that utilize the mixture model for multiple testing. The procedures are illustrated with applications to a gene expression profiling experiment in prostate cancer.Comment: Published in at http://dx.doi.org/10.1214/193940307000000176 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Robust Bayesian inference via coarsening

The standard approach to Bayesian inference is based on the assumption that the distribution of the data belongs to the chosen model class. However, even a small violation of this assumption can have a large impact on the outcome of a Bayesian procedure. We introduce a simple, coherent approach to Bayesian inference that improves robustness to perturbations from the model: rather than condition on the data exactly, one conditions on a neighborhood of the empirical distribution. When using neighborhoods based on relative entropy estimates, the resulting "coarsened" posterior can be approximated by simply tempering the likelihood---that is, by raising it to a fractional power---thus, inference is often easily implemented with standard methods, and one can even obtain analytical solutions when using conjugate priors. Some theoretical properties are derived, and we illustrate the approach with real and simulated data, using mixture models, autoregressive models of unknown order, and variable selection in linear regression

ON THE SMALL SAMPLE PROPERTIES OF DICKEY FULLER AND MAXIMUM LIKELIHOOD UNIT ROOT TESTS ON DISCRETE-SAMPLED SHORT-TERM INTEREST RATES

Testing for unit roots in short-term interest rates plays a key role in the empirical modelling of these series. It is widely assumed that the volatility of interest rates follows some time-varying function which is dependent of the level of the series. This may cause distortions in the performance of conventional tests for unit root nonstationarity since these are typically derived under the assumption of homoskedasticity. Given the relative unfamiliarity on the issue, we conducted an extensive Monte Carlo investigation in order to assess the performance of the DF unit root tests, and examined the effects on the limiting distributions of test procedures (t- and likelihood ratio tests) based on maximum likelihood estimation of models for short-term rates with a linear drift.Unit root, interest rates, CKLS model.

Analysis of rocket engine injection combustion processes

Mixing methodology improvement for the JANNAF DER and CICM injection/combustion analysis computer programs was accomplished. ZOM plane prediction model development was improved for installation into the new standardized DER computer program. An intra-element mixing model developing approach was recommended for gas/liquid coaxial injection elements for possible future incorporation into the CICM computer program

On the Small Sample Properties of Dickey Fuller and Maximum Likelihood Unit Root Tests on Discrete-Sampled Short-Term Interest Rates

Testing for unit roots in short-term interest rates plays a key role in the empirical modelling of these series. It is widely assumed that the volatility of interest rates follows some time-varying function which is dependent of the level of the series. This may cause distortions in the performance of conventional tests for unit root nonstationarity since these are typically derived under the assumption of homoskedasticity. Given the relative unfamiliarity on the issue, we conducted an extensive Monte Carlo investigation in order to assess the performance of the DF unit root tests, and examined the effects on the limiting distributions of test procedures (t- and likelihood ratio tests) based on maximum likelihood estimation of models for short-term rates with a linear drift.Unit root, interest rates, CKLS model.

A Kolmogorov-Smirnov test for the molecular clock on Bayesian ensembles of phylogenies

Divergence date estimates are central to understand evolutionary processes and depend, in the case of molecular phylogenies, on tests of molecular clocks. Here we propose two non-parametric tests of strict and relaxed molecular clocks built upon a framework that uses the empirical cumulative distribution (ECD) of branch lengths obtained from an ensemble of Bayesian trees and well known non-parametric (one-sample and two-sample) Kolmogorov-Smirnov (KS) goodness-of-fit test. In the strict clock case, the method consists in using the one-sample Kolmogorov-Smirnov (KS) test to directly test if the phylogeny is clock-like, in other words, if it follows a Poisson law. The ECD is computed from the discretized branch lengths and the parameter $\lambda$ of the expected Poisson distribution is calculated as the average branch length over the ensemble of trees. To compensate for the auto-correlation in the ensemble of trees and pseudo-replication we take advantage of thinning and effective sample size, two features provided by Bayesian inference MCMC samplers. Finally, it is observed that tree topologies with very long or very short branches lead to Poisson mixtures and in this case we propose the use of the two-sample KS test with samples from two continuous branch length distributions, one obtained from an ensemble of clock-constrained trees and the other from an ensemble of unconstrained trees. Moreover, in this second form the test can also be applied to test for relaxed clock models. The use of a statistically equivalent ensemble of phylogenies to obtain the branch lengths ECD, instead of one consensus tree, yields considerable reduction of the effects of small sample size and provides again of power.Comment: 14 pages, 9 figures, 8 tables. Minor revision, additin of a new example and new title. Software: https://github.com/FernandoMarcon/PKS_Test.gi

Continuous Empirical Characteristic Function Estimation of Mixtures of Normal Parameters

This paper develops an e±cient method for estimating the discrete mix- tures of normal family based on the continuous empirical characteristic function (CECF). An iterated estimation procedure based on the closed form objective distance function is proposed to improve the estimation effciency. The results from the Monte Carlo simulation reveal that the CECF estimator produces good finite sample properties. In particular, it outperforms the discrete type of methods when the maximum likelihood estimation fails to converge. An empirical example is provided for illustrative purposes.Empirical characteristic function; Mixtures of normal.