The exact distribution of the maximum, minimum and the range of multinomial/dirichlet and multivariate hypergeometric frequencies

The exact distribution of the maximum and minimum frequencies of Multinomial/Dirichlet and Multivariate Hypergeometric distributions of n balls in m urns is compactly represented as a product of stochastic matrices. This representation does not require equal urn probabilities, is invariant to urn order, and permits rapid calculation of exact probabilities. The exact distribution of the range is also obtained. These algorithms satisfy a long-standing need for routines to compute exact Multinomial/Dirichlet and Multivariate Hypergeometric maximum, minimum, and range probabilities in statistical computation libraries and software packages.

A brief review of recent research trends on applications of computational and statistical techniques in financial and business intelligence

Artificial neural networks and statistical techniques like decision trees, discriminant analysis, logistic regression and survival analysis play a crucial role in Business Intelligence. These predictive analytical tools exploit patterns found in historical data to make predictions about future events. In this paper we have shown some recent developments of a few of these techniques in financial and business intelligence applications like fraud detection, bankruptcy prediction and credit rating scoring.

Changing philosophies and tools for statistical inferences in behavioral ecology

Recent developments in ecological statistics have reached behavioral ecology, and an increasing number of studies now apply analytical tools that incorporate alternatives to the conventional null hypothesis testing based on significance levels. However, these approaches continue to receive mixed support in our field. Because our statistical choices can influence research design and the interpretation of data, there is a compelling case for reaching consensus on statistical philosophy and practice. Here, we provide a brief overview of the recently proposed approaches and open an online forum for future discussion (https://bestat.ecoinformatics.org/). From the perspective of practicing behavioral ecologists relying on either correlative or experimental data, we review the most relevant features of information theoretic approaches, Bayesian inference, and effect size statistics. We also discuss concerns about data quality, missing data, and repeatability. We emphasize the necessity of moving away from a heavy reliance on statistical significance while focusing attention on biological relevance and effect sizes, with the recognition that uncertainty is an inherent feature of biological data. Furthermore, we point to the importance of integrating previous knowledge in the current analysis, for which novel approaches offer a variety of tools. We note, however, that the drawbacks and benefits of these approaches have yet to be carefully examined in association with behavioral data. Therefore, we encourage a philosophical change in the interpretation of statistical outcomes, whereas we still retain a pluralistic perspective for making objective statistical choices given the uncertainties around different approaches in behavioral ecology. We provide recommendations on how these concepts could be made apparent in the presentation of statistical outputs in scientific papers.

Performance evaluation of open source seismic data processing packages

In many businesses, including hydrocarbon industries, reducing cost is of high priority. Although hydrocarbon industries appear able to afford the expensive computing infrastructure and software packages used to process seismic data in the search for hydrocarbon traps, it is always imperative to find ways to minimize cost. Seismic processing costs can be significantly reduced by using inexpensive, open source seismic data processing packages. However, hydrocarbon industries question the processing performance capability of open source packages, claiming that their seismic functions are less integrated and provide almost no technical guarantees for one to use. The objective of this paper is to demonstrate, through a comparative analysis, that open source seismic data processing packages are capable of executing the required seismic functions on an actual industrial workload. To achieve this objective we investigate whether or not open source seismic data processing packages can be executed using the same set of seismic data through data format conversions, and whether or not they can achieve reasonable performance and speedup when executing parallel seismic functions on a HPC cluster. Among the few open source packages available on the Internet, the subjects of our study are two popular packages: Seismic UNIX (SU) and Madagascar.

Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association

Recognising behaviours of multiple people, especially high-level behaviours, is an important task in surveillance systems. When the reliable assignment of people to the set of observations is unavailable, this task becomes complicated. To solve this task, we present an approach, in which the hierarchical hidden Markov model (HHMM) is used for modeling the behaviour of each person and the joint probabilistic data association filters (JPDAF) is applied for data association. The main contributions of this paper lie in the integration of multiple HHMMs for recognising high-level behaviours of multiple people and the construction of the Rao-Blackwellised particle filters (RBPF) for approximate inference. Preliminary experimental results in a real environment show the robustness of our integrated method in behaviour recognition and its advantage over the use of Kalman filter in tracking people.

Statistical models for cancer screening

This paper reviews the application of statistical models to planning and evaluating cancer screening programmes. Models used to analyse screening strategies can be classified as either surface models, which consider only those events which can be directly observed such as disease incidence, prevalence or mortality, or deep models, which incorporate hypotheses about the disease process that generates the observed events. This paper focuses on the latter type. These can be further classified as analytic models, which use a model of the disease to derive direct estimates of characteristics of the screening procedure and its consequent benefits, and simulation models, which use the disease model to simulate the course of the disease in a hypothetical population with and without screening and derive measures of the benefit of screening from the simulation outcomes. The main approaches to each type of model are described and an overview given of their historical development and strengths and weaknesses. A brief review of fitting and validating such models is given and finally a discussion of the current state of, and likely future trends in, cancer screening models is presented.

Utilisation of primary total knee joint replacements across socioeconomic status in the Barwon Statistical Division, Australia 2006-7 : a cross-sectional study

Objectives There are few Australian data that examine the association between total knee joint replacement (TKR) utilisation and socioeconomic status (SES). This study examined TKR surgeries with a diagnosis of osteoarthritis (OA) performed for residents of Barwon Statistical Division (BSD) for 2006–2007.

Design Cross-sectional.

Setting BSD, South-eastern Victoria, Australia

Participants All patients who underwent a TKR for OA, 2006–2007, and whose residential postcode was identified as within the BSD of Australia, and for whom SES data were available, were eligible for inclusion.

Primary outcome measure Primary TKR data ascertained from the Australian Orthopaedic Association National Joint Replacement Registry. Residential addresses were matched with the Australian Bureau of Statistics census data, and the Index of Relative Socioeconomic Disadvantage was used to determine SES, categorised into quintiles whereby quintile 1 indicated the most disadvantaged and quintile 5 the least disadvantaged. Age-specific and sex-specific rates of TKR utilisation per 1000 person-years were reported for 10-year age bands.

Results Females accounted for 62.7% of the 691 primary TKR surgeries performed during 2006–2007. The greatest utilisation rates of TKR in males was 7.6 observed in those aged >79 years, and in 10.2 in females observed in those aged 70–79 years. An increase in TKR was observed for males in SES quintile four compared to quintile 1 in which the lowest utilisation which was observed (p=0.04). No differences were observed in females across SES quintiles.

Conclusions Further investigation is warranted on a larger scale to examine the role that SES may play in TKR utilisation, and to determine whether any social disparities in TKR utilisation reflect health system biases or geographic differences.

Interpreting electrochemical noise resistance as a statistical linear polarisation resistance

A theoretical analysis on the electrochemical noise resistance is carried out based on basic electrochemical corrosion theory. It is proven that the noise resistance is equivalent to the polarisation resistance and is in fact a special form of linear polarisation resistance, namely statistical linear polarisation resistance.

Using inquiry-based learning to develop and assess statistical reasoning

When learning, teaching and assessments of statistical content are based on an inquiry cycle with contextual linkages, then this results in improved learner performances. If assessment questions in statistics are linked conceptually within an appropriate context, as opposed to being fragmented, then improved learner performances in statistical reasoning results.

Our natives and wild blacks : enumeration as a statistical dimension of sovereignty in colonial Western Australia

This paper investigates the Western Australian colonial authorities' attempts at defining and categorising a "politically relevant" Aboriginal population from first settlement in 1829 until 1850. Studies of colonial enumeration allow us to understand how colonial authorities viewed the spaces and boundaries of settlement and beyond, and who would be included as part of the community inhabiting that space. Enumeration of Aboriginal people in this period mirrored the Western Australian colonial authorities' conception of their sovereignty: the territory which they could effectively control was not the entire western third of the continent, as the map dictated, but rather the surveyed country, within the "limits of settlement." While other studies of colonial census making reveal enumeration as an instrument of control, this paper identifies colonial census making about Indigenous Western Australians in this period as an instance of state incapacity to govern and control. While "control" was the colonial authorities' key objective in their enumerations, the census reports reveal their inability to know the Aboriginal population.

Large-scale statistical modeling of motion patterns : a Bayesian nonparametric approach

We propose a novel framework for large-scale scene understanding in static camera surveillance. Our techniques combine fast rank-1 constrained robust PCA to compute the foreground, with non-parametric Bayesian models for inference. Clusters are extracted in foreground patterns using a joint multinomial+Gaussian Dirichlet process model (DPM). Since the multinomial distribution is normalized, the Gaussian mixture distinguishes between similar spatial patterns but different activity levels (eg. car vs bike). We propose a modification of the decayed MCMC technique for incremental inference, providing the ability to discover theoretically unlimited patterns in unbounded video streams. A promising by-product of our framework is online, abnormal activity detection. A benchmark video and two surveillance videos, with the longest being 140 hours long are used in our experiments. The patterns discovered are as informative as existing scene understanding algorithms. However, unlike existing work, we achieve near real-time execution and encouraging performance in abnormal activity detection.