Comparison of Two Methods for Caluculating the Partition Functions of Various Spatial Statistical Models

Likelihood computation in spatial statistics requires accurate and efficient calculation of the normalizing constant (i.e. partition function) of the Gibbs distribution of the model. Two available methods to calculate the normalizing constant by Markov chain Monte Carlo methods are compared by simulation experiments for an Ising model, a Gaussian Markov field model and a pairwise interaction point field model.

Performance of statistical inference for block classification in reducing blocking artefact in video coded images

This paper presents a performance study of four statistical test algorithms used to identify smooth image blocks in order to filter the reconstructed image of a video coded image. The four algorithms considered are the Coefficient of Variation (CV), Exponential Entropy of Pal and Pal (E), Shannon's (Logarithmic) Entropy (H), and Quadratic Entropy (Q). These statistical algorithms are employed to distinguish between smooth and textured blocks in a reconstructed image. The linear filtering is carried out on the smooth blocks of the image to reduce the blocking artefact. The rationale behind applying the filter on the smooth blocks only is that the blocking artefact is visually more prominent in the smooth region of an image rather than in the textured region.

Benchmarking new statistical techniques in ration-based modelling of corporate collapse

Up until 1979, Multiple Discriminant Analysis (MDA) was the primary multivariate methodological approaches to ratio-based modelling of corporate collapse. However, as new statistical tools became available, researchers started testing them with the primary objective of deriving models that would at least do as good a job as MDA, but that rely on fewer assumptions. Regardless of which methodological approach was chosen, most were compared to MDA. This paper analyses 84 studies on ratio based modelling of corporate collapse over the period 1968 to 2004. The results indicate that when MDA was not the primary methodology it was the benchmark of choice for comparison; thereby, demonstrating its importance as a foundation multivariate methodological approach in signalling corporate collapse.

Identifying cis-regulatory elements by statistical analysis and phylogenetic footprinting and analyzing their coexistence and related gene ontology

Discovery of cis-regulatory elements in gene promoters is a highly challenging research issue in computational molecular biology. This paper presents a novel approach to searching putative cis-regulatory elements in human promoters by first finding 8-mer sequences of high statistical significance from gene promoters of humans, mice, and Drosophila melanogaster, respectively, and then identifying the most conserved ones across the three species (phylogenetic footprinting). In this study, a conservation analysis on both closely related species (humans and mice) and distantly related species (humans/mice and Drosophila) is conducted not only to examine more candidates but also to improve the prediction accuracy. We have found 124 putative cis-regulatory elements and grouped these into 20 clusters. The investigation on the coexistence of these clusters in human gene promoters reveals that SP1, EGR, and NRF-1 are the dominant clusters appearing in the combinatorial combination of up to five clusters. Gene Ontology (GO) analysis also shows that many GO categories of transcription factors binding to these cis-regulatory elements match the GO categories of genes whose promoters contain these elements. Compared with previous research, the contribution of this study lies not only in the finding of new cis-regulatory elements, but also in its pioneering exploration on the coexistence of discovered elements and the GO relationship between transcription factors and regulated genes. This exploration verifies the putative cis-regulatory elements that have been found from this study and also gives new insight on the regulation mechanisms of gene expression.

A statistical-driven approach for automatic classification of events in AFL video highlights

Due to the repetitive and lengthy nature, automatic content-based summarization is essential to extract a more compact and interesting representation of sport video. State-of-the art approaches have confirmed that high-level semantic in sport video can be detected based on the occurrences of specific audio and visual features (also known as cinematic). However, most of them still rely heavily on manual investigation to construct the algorithms for highlight detection. Thus, the primary aim of this paper is to demonstrate how the statistics of cinematic features within play-break sequences can be used to less-subjectively construct highlight classification rules. To verify the effectiveness of our algorithms, we will present some experimental results using six AFL (Australian Football League) matches from different broadcasters. At this stage, we have successfully classified each play-break sequence into: goal, behind, mark, tackle, and non-highlight. These events are chosen since they are commonly used for broadcasted AFL highlights. The proposed algorithms have also been tested successfully with soccer video.

Towards universal and statistical-driven heuristics for automatic classification of sports video events

Researchers worldwide have been actively seeking for the most robust and powerful solutions to detect and classify key events (or highlights) in various sports domains. Most approaches have employed manual heuristics that model the typical pattern of audio-visual features within particular sport events To avoid manual observation and knowledge, machine-learning can be used as an alternative approach. To bridge the gaps between these two alternatives, an attempt is made to integrate statistics into heuristic models during highlight detection in our investigation. The models can be designed with a modest amount of domain-knowledge, making them less subjective and more robust for different sports. We have also successfully used a universal scope of detection and a standard set of features that can be applied for different sports that include soccer, basketball and Australian football. An experiment on a large dataset of sport videos, with a total of around 15 hours, has demonstrated the effectiveness and robustness of our
aIlgorithms.

The successful school: a genuine trend or a statistical artifice?

Schools are increasingly being expected to make improvements based on data about students' learning outcomes. Such an expectation implies that principals, teachers and key personnel within systems can read and act upon the data available. There is evidence, however, that many people have poor understanding of statistical information, and that many factors inside and outside the school have an effect on students' outcomes. This study considers one primary school's data from statewide testing programs. Trends across time are considered as a basis for making judgments about the school's performance in improving students' learning outcomes in literacy and numeracy.

Statistical analysis of finite element modeling in sheet metal forming and springback analysis

This work focuses on development of a method to statistically study forming and springback problems of TRansformation Induced Plasticity (TRIP) through an industrial case study. A Design of Experiments (DOE) approach was used to study the sensitivity of predictions to four user input parameters in implicit and explicit sheet metal forming codes. Numerical results were compared to experimental measurements of parts stamped in an industrial production line. The accuracy of forming strain predictions for TRIP steel were comparable with conventional steel, but the springback predictions of TRIP steel were far less accurate. The statistical importance of selected parameters for forming and springback prediction is also discussed. Changes of up to ±10% in Young's modulus and coefficient of friction were found to be insignificant in improving or deteriorating the statistical correlation of springback accuracies.

Microarray classification using statistical learning and decision tree algorithms

This considers the challenging task of cancer prediction based on microarray data for the medical community. The research was conducted on mostly common cancers (breast, colon, long, prostate and leukemia) microarray data analysis, and suggests the use of modern machine learning techniques to predict cancer.

Statistical properties and taxonomies of human untranslated exons

Two taxonomies for the accurate classification of human and predicted exons were produced. Based on these taxonomies important statistical properties of untranslated exons useful for improving automated genefinding efforts were calculated. Finally an important correlation between the energy and the information content in the human genome was identified.