An R-package for the Estimation and Testing of Multiple Covariates and Biomarker Interactions for Survival Data Based on Local Partial Likelihood

When we study the variables that a ffect survival time, we usually estimate their eff ects by the Cox regression model. In biomedical research, e ffects of the covariates are often modi ed by a biomarker variable. This leads to covariates-biomarker interactions. Here biomarker is an objective measurement of the patient characteristics at baseline. Liu et al. (2015) has built up a local partial likelihood bootstrap model to estimate and test this interaction e ffect of covariates and biomarker, but the R code developed by Liu et al. (2015) can only handle one variable and one interaction term and can not t the model with adjustment to nuisance variables. In this project, we expand the model to allow adjustment to nuisance variables, expand the R code to take any chosen interaction terms, and we set up many parameters for users to customize their research. We also build up an R package called "lplb" to integrate the complex computations into a simple interface. We conduct numerical simulation to show that the new method has excellent fi nite sample properties under both the null and alternative hypothesis. We also applied the method to analyze data from a prostate cancer clinical trial with acid phosphatase (AP) biomarker.

The Punk-Rock King: Musical Anachronism in Period Film

Music has a powerful indexical ability to evoke particular times and places. Such an ability has been exploited at length by the often-elaborate soundscapes of period films, which regularly utilise incidental scores and featured period songs to help root their narrative action in past times, and to immerse their audiences in the sensibilities of a different age. However, this article will begin to examine the ways in which period film soundtracks can also be used to complicate a narrative sense of time and place through the use of ‘musical anachronism’: music conspicuously ‘out of time’ with the temporality depicted on screen. Through the analysis of a sequence from the film W.E. (Madonna, 2011) and the consideration of existing critical and conceptual contexts, this article will explore how anachronistic soundtracks can function beyond ‘postmodern novelty’ or ‘nuisance’ to historical verisimilitude, instead offering alternative modes of engagement with story and history.

Recovering Natural History: Modeling Cardiovascular Biomarkers in the Presence of Endogenous Medication Use

Thesis (Ph.D.)--University of Washington, 2016-08

Asymptotic Change-Point Analysis of the Dependencies in Time Series

In recent papers, Wied and his coauthors have introduced change-point procedures to detect and estimate structural breaks in the correlation between time series. To prove the asymptotic distribution of the test statistic and stopping time as well as the change-point estimation rate, they use an extended functional Delta method and assume nearly constant expectations and variances of the time series. In this thesis, we allow asymptotically infinitely many structural breaks in the means and variances of the time series. For this setting, we present test statistics and stopping times which are used to determine whether or not the correlation between two time series is and stays constant, respectively. Additionally, we consider estimates for change-points in the correlations. The employed nonparametric statistics depend on the means and variances. These (nuisance) parameters are replaced by estimates in the course of this thesis. We avoid assuming a fixed form of these estimates but rather we use "blackbox" estimates, i.e. we derive results under assumptions that these estimates fulfill. These results are supplement with examples. This thesis is organized in seven sections. In Section 1, we motivate the issue and present the mathematical model. In Section 2, we consider a posteriori and sequential testing procedures, and investigate convergence rates for change-point estimation, always assuming that the means and the variances of the time series are known. In the following sections, the assumptions of known means and variances are relaxed. In Section 3, we present the assumptions for the mean and variance estimates that we will use for the mean in Section 4, for the variance in Section 5, and for both parameters in Section 6. Finally, in Section 7, a simulation study illustrates the finite sample behaviors of some testing procedures and estimates.

Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work.

Aquatic weeds, their impacts on fisheries and environment, and their control

Weeds are plants growing in environments where they are undesirable. Aquatic weeds in fresh waters are nuisance or noxious plants growing in association with water in lakes, impoundment, rivers, canals, wetlands, etc. Some waterweeds cause very big financial loss through the socio economic, environmental and ecological impacts they inflict; and through the effort and expense required for their control. Other waterweeds are simply nuisance plants that cause minimal impacts. This paper is intended to introduce aquatic weeds outlining their characteristics, the main socio-economic and environmental impacts associated with them, and the control strategies often applied for their management.

Wildlife as a Priority in Park Development: A Study of Illinois Park Professionals

The purpose of this thesis is to consider the factors that impact decision making in city park settings, with specific emphasis given to wildlife. Additionally, professional bias was considered as a possible response determinant. Studies connecting perceptions of wildlife and Illinois park managers have been rare or nonexistent, but offer the potential for the improvement of management strategies and recreational opportunities. Data was collected using mixed methods. City recreation practitioners statewide were invited to complete a self-administered questionnaire considering wildlife as a decision-making factor in land acquisition or restoration decisions. A small follow-up sample of park managers was interviewed via telephone for further explanation of their response. Analysis of responses from questionnaires and interviews suggested that wildlife habitat is a factor in land use decision making, but is not considered one of the highest importance. Respondents identified that nuisance wildlife, access to wildlife, and public value of wildlife were also factors in decision making. Factors associated with a high-ranking of the importance of wildlife were agencies with a high number of natural area acres, a high number of overall park acreage, personnel devoted to natural area management, the presence of hiking trails, and cities with a large population. Professional bias of recreation managers was suggested via anecdotal interview data, but could not be empirically connected with wildlife-related decision-making processes, as no managers identified themselves as having completed formal wildlife-related training. As a result, management implications include separate training for both practitioners and public. This study broadens the understanding of wildlife management in city park settings, and reaffirms that further understanding of public and pracitioner values of wildlife will lead to improved land use decisions and recreationally valuable experiences.

Les macro-déchets en mer

This text presents the general problem of nuisance and pollution created by macro-waste at sea. We clarify three main types of pollution: that of coasts, that of the sea bed and that of the open sea. The article leans on scientific data: report of the state of contamination of the coasts of France by macro waste (1982), results of works and oceanographic sea bed observation campaigns, evaluation of the contamination by micro-plastics floating on surface and concentrating in the big oceanic gyres. A paragraph is dedicated to the impacts on the ecosystems, an other one on the taken measures to fight against those macro-waste. The specific case of New Caledonia is not forgotten and in conclusion we underline the necessity of an education of the populations in terms of Eco responsibility, which appears to be a significant factor of regression of this type of nuisance that affects all the seas of the world

Performance and Robustness Analysis of Co-Prime and Nested Sampling

Coprime and nested sampling are well known deterministic sampling techniques that operate at rates significantly lower than the Nyquist rate, and yet allow perfect reconstruction of the spectra of wide sense stationary signals. However, theoretical guarantees for these samplers assume ideal conditions such as synchronous sampling, and ability to perfectly compute statistical expectations. This thesis studies the performance of coprime and nested samplers in spatial and temporal domains, when these assumptions are violated. In spatial domain, the robustness of these samplers is studied by considering arrays with perturbed sensor locations (with unknown perturbations). Simplified expressions for the Fisher Information matrix for perturbed coprime and nested arrays are derived, which explicitly highlight the role of co-array. It is shown that even in presence of perturbations, it is possible to resolve $O(M^2)$ under appropriate conditions on the size of the grid. The assumption of small perturbations leads to a novel ``bi-affine" model in terms of source powers and perturbations. The redundancies in the co-array are then exploited to eliminate the nuisance perturbation variable, and reduce the bi-affine problem to a linear underdetermined (sparse) problem in source powers. This thesis also studies the robustness of coprime sampling to finite number of samples and sampling jitter, by analyzing their effects on the quality of the estimated autocorrelation sequence. A variety of bounds on the error introduced by such non ideal sampling schemes are computed by considering a statistical model for the perturbation. They indicate that coprime sampling leads to stable estimation of the autocorrelation sequence, in presence of small perturbations. Under appropriate assumptions on the distribution of WSS signals, sharp bounds on the estimation error are established which indicate that the error decays exponentially with the number of samples. The theoretical claims are supported by extensive numerical experiments.

Current Legal Rules Benefit Spray Applicators When It Comes to Pesticide Drift

Homeowners, landowners, pesticide applicators, and farmers are concerned about pesticide drift. It may injure a homeowner’s garden or flowers or ruin a neighboring farmer’s crop. While no Maryland court has considered the issue of liability from pesticide drift, courts in other states have. These decisions provide some guidance on how a Maryland court might handle the issue. Depending on the facts of the drift case, pesticide applicators and farmers could owe damages for nuisance or trespass case, or for uses inconsistent with the pesticide label.

Attitudes toward mental illness among college students in Nuevo Leon, México = Actitudes hacia las enfermedades mentales en estudiantes universitarios de Nuevo León, México

Stigma associated with mental illness has detrimental effects on the treatment and prevention of these diseases. The aim of this study was to analyze attitudes toward mental illness in a sample of university students in Nuevo Leon, Mexico. Results. Nine hundred and forty-three students were surveyed, 66.9% believe that genetic and familial factors are the cause of mental illness. Among 20-30% believe that people with mental illness are a nuisance for people; between 12-14% would be ashamed of having a family member with mental illness and people know it; and 61.8% would be able to maintain a friendship with a person who have mental illness. Conclusions. Over 50% of respondents have favorable attitudes towards patients with mental illness and less than 30% attitudes of social distancing.

Population estimates and behavioral analyses of managed cat (Felis Catus) colonies located in Miami-Dade County, Florida, parks

The purpose of my study was to collect data on managed cat (Felis catus) colonies located in two Miami-Dade County, Florida, parks, in order to test the following assertions put forward by proponents of the colonies: 1) Managed cat colonies will decline in size over time and 2) The territorial behavior of cats living in established cat colonies will prevent additional cats from joining. I collected observational and photographic capture-recapture data in order to track colony population dynamics. Behavioral data were also collected in order to understand the role that cat behavior plays in influencing colony population dynamics. My results do not support the assertion that colonies will decline over time. Instead, my findings demonstrate that the establishment of colonies on public lands encourages dumping of cats and creates an attractive nuisance. Furthermore, my behavioral analysis suggests that territorial behavior does not play a role in excluding new cats.