Count Time Series and Discrete Renewal Processes

Most data collected over time has some degree of periodicity (i.e. seasonally varying traits). Climate, stock prices, football season, energy consumption, wildlife sightings, and holiday sales all have cyclical patterns. It should come as no surprise that models that incorporate periodicity are paramount in the study of time series. The following work devises time series models for counts (integer-values) that are periodic and stationary. Foundational work is rst done in constructing a stationary periodic discrete renewal process (SPDRP). The dynamics of the SPDRP are mathematically interesting and have many modeling applications, expositions largely unexplored here. This work develops a SPDRP as a generation mechanism to produce a stationary count time series models with many desirable characteristics, including periodicity, negative autocovariances and long-memory. After development of the SPDRP univariate count models are generalized into multiple dimensions. A multivariate renewal process has many interrelated stochastic processes. The resulting multivariate model has all the desirable properties of its univariate kin, but can also have negative autocovariances between marginal components of the series. To our knowledge, this trait is seldom achieved in current multivariate count methods in tandem with long-memory and periodicit

Latent Gaussian Count Time Series Modeling

This paper develops theory and methods for the copula modeling of stationary count time series. The techniques use a latent Gaussian process and a distributional transformation to construct stationary series with very flexible correlation features that can have any pre-specified marginal distribution, including the classical Poisson, generalized Poisson, negative binomial, and binomial count structures. A Gaussian pseudo-likelihood estimation paradigm, based only on the mean and autocovariance function of the count series, is developed via some new Hermite expansions. Particle filtering methods are studied to approximate the true likelihood of the count series. Here, connections to hidden Markov models and other copula likelihood approximations are made. The efficacy of the approach is demonstrated and the methods are used to analyze a count series containing the annual number of no-hitter baseball games pitched in major league baseball since 1893

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

Imagining an Imperial Modernity: Universities and the West African Roots of Colonial Development

© 2016 Informa UK Limited, trading as Taylor & Francis GroupThis article takes the formation and work of the ‘Elliot’ Commission on Higher Education in West Africa (1943–45) to reconsider the roots of British colonial development. Late colonial universities were major development projects, although they have rarely been considered as such. Focusing particularly on the Nigerian experience and the controversy over Yaba Higher College (founded 1934), the article contends that late colonial plans for universities were not produced in Britain and then exported to West African colonies. Rather, they were formed through interactions between agendas and ideas with roots in West Africa, Britain and elsewhere. These debates exhibited asymmetries of power but produced some consensus about university development. African and British actors conceptualised modern education by combining their local concerns with a variety of supra-local geographical frames for development, which included the British Empire and the individual colony. The British Empire did not in this case forestall development, but shaped the ways in which development was conceived

Correction: Priorities and interactions of sustainable development goals (SDGs) with focus on wetlands. Water 2019, 11, 619 doi: 10.3390/w11030619

10.3390/w12010088Water (Switzerland)1218

ECONOMIC DIVERSIFICATION THROUGH A KNOWLEDGE-BASED ECONOMY IN THE UNITED ARAB EMIRATES: A STUDY OF PROGRESS TOWARD VISION 2021

With the 2010 release of Vision 2021, the government's comprehensive societal and economic vision for the future, the United Arab Emirates (UAE) laid out a path to diversify its economy. This plan moves the UAE away from resource dependence to a knowledge-based economy less reliant on natural resources and physical labor. This thesis explores the economic history of the United Arab Emirates, its previous diversification efforts, and its proposed way forward with Vision 2021. The author evaluates progress made up to this point using economic data and key performance indicators outlined in Vision 2021. This evaluation shows that although the UAE has made significant investment into diversification efforts, there has not yet been the expected return on investment. In order to fulfill the plan’s aspirations, the UAE will need to make significant strides over the next two years.http://archive.org/details/economicdiversif1094562272Captain, United States Air ForceApproved for public release; distribution is unlimited

Small-sample Reinforcement Learning: Improved Policies Using Synthetic Data

Reinforcement learning (RL) concerns algorithms tasked with learning optimal control policies by interacting with or observing a system. In computer science and other fields in which RL originated, large sample sizes are the norm, because data can be generated at will from a generative model. Recently, RL methods have been adapted for use in clinical trials, resulting in much smaller sample sizes. Nonparametric methods are common in RL, but are likely to over-generalize when limited data is available. This paper proposes a novel methodology for learning optimal policies by leveraging the researcher\u27s partial knowledge about the probability transition structure into an approximate generative model from which synthetic data can be produced. Our method is applied to a scenario where the researcher must create a medical prescription policy for managing a disease with sporadically appearing symptoms

Superpositioned stationary count time series

This paper probabilistically explores a class of stationary count time series models built by superpositioning (or otherwise combining) independent copies of a binary stationary sequence of zeroes and ones. Superpositioning methods have proven useful in devising stationary count time series having prespecified marginal distributions. Here, basic properties of this model class are established and the idea is further developed. Specifically, stationary series with binomial, Poisson, negative binomial, discrete uniform, and multinomial marginal distributions are constructed; other marginal distributions are possible. Our primary goal is to derive the autocovariance function of the resulting series