The Integrated Model of Sport Confidence: A Canonical Correlation and Mediational Analysis

The main purpose of the study was to examine crucial parts of Vealey’s (2001) integrated framework hypothesizing that sport confidence is a mediating variable between sources of sport confidence (including achievement, self-regulation, and social climate) and athletes’ affect in competition. The sample consisted of 386 athletes, who completed the Sources of Sport Confidence Questionnaire, Trait Sport Confidence Inventory, and Dispositional Flow Scale-2. Canonical correlation analysis revealed a confidence-achievement dimension underlying flow. Bias-corrected bootstrap confidence intervals in AMOS 20.0 were used in examining mediation effects between source domains and dispositional flow. Results showed that sport confidence partially mediated the relationship between achievement and  self-regulation domains and flow, whereas no significant mediation was found for social climate. On a subscale level, full mediation models emerged for achievement and flow dimensions of challenge–skills balance, clear goals, and concentration on the task at hand.

GIS-based mapping and statistical analysis of air pollution and mortality in Brisbane, Australia

In this thesis, the relationship between air pollution and human health has been investigated utilising Geographic Information System (GIS) as an analysis tool. The research focused on how vehicular air pollution affects human health. The main objective of this study was to analyse the spatial variability of pollutants, taking Brisbane City in Australia as a case study, by the identification of the areas of high concentration of air pollutants and their relationship with the numbers of death caused by air pollutants. A correlation test was performed to establish the relationship between air pollution, number of deaths from respiratory disease, and total distance travelled by road vehicles in Brisbane. GIS was utilized to investigate the spatial distribution of the air pollutants. The main finding of this research is the comparison between spatial and non-spatial analysis approaches, which indicated that correlation analysis and simple buffer analysis of GIS using the average levels of air pollutants from a single monitoring station or by group of few monitoring stations is a relatively simple method for assessing the health effects of air pollution. There was a significant positive correlation between variable under consideration, and the research shows a decreasing trend of concentration of nitrogen dioxide at the Eagle Farm and Springwood sites and an increasing trend at CBD site. Statistical analysis shows that there exists a positive relationship between the level of emission and number of deaths, though the impact is not uniform as certain sections of the population are more vulnerable to exposure. Further statistical tests found that the elderly people of over 75 years age and children between 0-15 years of age are the more vulnerable people exposed to air pollution. A non-spatial approach alone may be insufficient for an appropriate evaluation of the impact of air pollutant variables and their inter-relationships. It is important to evaluate the spatial features of air pollutants before modeling the air pollution-health relationships.

An analysis of NSW rural property market : 1990-2010

Rural property in Australia has seen significant market resurgence over the past 3 years, with improved seasonal conditions in a number of states, improved commodity prices and a greater interest and purchase of rural land by major international corporations and investment institutions. Much of this change in perspective in relation to rural property as an asset class can be linked to the food shortage of 2007 and the subsequent interest by many countries in respect to food security. This paper will address the total and capital return performance of a major agricultural area and compare these returns on the basis of both location of land and land use. The comparison will be used to determine if location or actual land use has a greater influence on rural property capital and income returns. This performance analysis is based on over 40,000 rural sales transactions. These transactions cover all market based rural property transactions in New South Wales, Australia for the period January 1990 to December 2010. Correlation analysis and investment performance analysis has also been carried out to determine the possible relationships between location and land use and subsequent changes in rural land capital values.

Kernel analysis on Grassmann manifolds for action recognition

Modelling video sequences by subspaces has recently shown promise for recognising human actions. Subspaces are able to accommodate the effects of various image variations and can capture the dynamic properties of actions. Subspaces form a non-Euclidean and curved Riemannian manifold known as a Grassmann manifold. Inference on manifold spaces usually is achieved by embedding the manifolds in higher dimensional Euclidean spaces. In this paper, we instead propose to embed the Grassmann manifolds into reproducing kernel Hilbert spaces and then tackle the problem of discriminant analysis on such manifolds. To achieve efficient machinery, we propose graph-based local discriminant analysis that utilises within-class and between-class similarity graphs to characterise intra-class compactness and inter-class separability, respectively. Experiments on KTH, UCF Sports, and Ballet datasets show that the proposed approach obtains marked improvements in discrimination accuracy in comparison to several state-of-the-art methods, such as the kernel version of affine hull image-set distance, tensor canonical correlation analysis, spatial-temporal words and hierarchy of discriminative space-time neighbourhood features.

Swimming crab Callinectes amnicola (Decapoda-Portunidae) capture analysis of a lagoon with high continental influence (Aby Lagoon, Cote d'Ivoire, West Africa)

The study describes the main causes of captures and productions decreasing of swimming crab Callinectes amnicola (Decapoda Portunidae) in Aby lagoon complex. For that, docks of two Sub Prefectures of Adiaké and Assini-Mafia respectively including the villages of Adiaké, Anga, Assomlan, Epleman, Aby and Man-Man, M'Bratty, Assini-Ngouankro and Assini-Mafia were studied from 2006 to 2009 and completed with previous results obtained from 1988 to 2005. Field investigators were identified by site/village and they recorded daily activities of fishermen (number of effective fishermen, number of gears and area of fishing, duration of fishing, types and quantity of bait) and landing of swimming crabs. During recent period of the study, total production decreased from 3742 tons in 2006 to 1500 tons in 2009. Matrix correlations and correlation analysis indicated that this downward trend was due to the increase of the number of fishermen, number of fishing gear, the decrease in female crabs capture and degradation of the environment related to gradual closure of the Assini-Mafia channel. Despite this decline, total production in Aby lagoon remained high compared to the productions of some lagoons of the country and the region. Given the importance of fishing swimming crabs in Aby lagoon, since it concerns many young people and it is a source of income, stringent measures for sustainable and responsible management must be taken and implemented as part of a co-management plan involving all stakeholders to sustainably manage the resource

Randomized nonlinear component analysis

Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved. Classical methods such as Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are ubiquitous in statistics. However, these techniques are only able to reveal linear re-lationships in data. Although nonlinear variants of PCA and CCA have been proposed, these are computationally prohibitive in the large scale. In a separate strand of recent research, randomized methods have been proposed to construct features that help reveal nonlinear patterns in data. For basic tasks such as regression or classification, random features exhibit little or no loss in performance, while achieving drastic savings in computational requirements. In this paper we leverage randomness to design scalable new variants of nonlinear PCA and CCA; our ideas extend to key multivariate analysis tools such as spectral clustering or LDA. We demonstrate our algorithms through experiments on real- world data, on which we compare against the state-of-the-art. A simple R implementation of the presented algorithms is provided.

Development of a biotic index using the correlation of protozoan communities with chemical water quality

A method of comparing data on protozoan communities with chemical parameters is presented. Using data from an extensive survey of the River Hanjiang in China, each species of protozoa has been given a species pollution value (SPV) related to its occurrence in waters with different degrees of pollution. A comprehensive chemical index is calculated for each site based on water quality standards for eight chemical parameters. The index is calculated from the relationship between the observed levels of each chemical at a site, compared with the limits of the drinking water quality standards of the People's Republic of China. From the distribution of each species at sites with differing chemical index values, a SPV is calculated. The SPV for each species is obtained by summing the logarithmic value of 10 times the chemical pollution divided by the number of chemical parameters, then divided by the stations where the species occurs. The community pollution value (CPV), which is the average SPVs of all protozoa at a site, is used to evaluate water quality. The CPV has been shown to have a close correlation with the degree of water pollution. It is not necessary for all the protozoa in a sample to have SPVs listed in this paper, provided at least 56% of the protozoa in a sample have an SPV value, the CPV will be applicable.

Analysis of the sea-level variability along the Chinese coast and estimation of the impact of a CO2-perturbed atmospheric circulation

The relationship between monthly sea-level data measured at stations located along the Chinese coast and concurrent large-scale atmospheric forcing in the period 1960-1990 is examined. It is found that sea-level varies quite coherently along the whole coast, despite the geographical extension of the station set. A canonical correlation analysis between sea-level and sea-level pressure (SLP) indicates that a great part of the sea-level variability can be explained by the action of the wind stress on the ocean surface. The relationship between sea-level and sea-level pressure is analyzed separately for the summer and winter half-years. In winter, one factor affecting sea-level variability at all stations is the SLP contrast between the continent and the Pacific Ocean, hence the intensity of the winter Monsoon circulation. Another factor that affects coherently all stations is the intensity of the zonal circulation at mid-latitudes. In the summer half year, on the other hand, the influence of SLP on sea-level is spatially less coherent: the stations in the Yellow Sea are affected by a more localized circulation anomaly pattern, whereas the rest of the stations is more directly connected to the intensity of the zonal circulation. Based on this analysis, statistical models (different for summer and winter) to hindcast coastal sealevel anomalies from the large-scale SLP field are formulated. These models have been tested by fitting their internal parameters in a test period and reproducing reasonably the sea-level evolution in an independent period. These statistical models are also used to estimate the contribution of the changes of the atmospheric circulation on sea-level along the Chinese coast in an altered climate. For this purpose the ouput of 150 year-long experiment with the coupled ocean-atmosphere model ECHAM1-LSG has been analyzed, in which the atmospheric concentration of greenhouse gases was continuously increased from 1940 until 2090, according to the Scenario A projection of the Intergovermental Panel on Climate Change. In this experiment the meridional (zonal) circulation relevant for sea-level tends to become weaker (stronger) in the winter half year and stronger (weaker) in summer. The estimated contribution of this atmospheric circulation changes to coastal sea-level is of the order of a few centimeters at the end of the integration, being in winter negative in the Yellow Sea and positive in the China Sea with opposite signs in the summer half-year.

Relationship between biomedical catheter surface properties and lubricity as determined using textural analysis and multiple regression analysis

In this study, the surface properties of and work required to remove 12 commercially available and developmental catheters from a model biological medium (agar), a measure of catheter lubricity, were characterised and the relationships between these properties were examined using multiple regression and correlation analysis. The work required for removal of catheter sections (7 cm) from a model biological medium (1% w/w agar) were examined using tensile analysis. The water wettability of the catheters were characterised using dynamic contact angle analysis, whereas surface roughness was determined using atomic force microscopy. Significant differences in the ease of removal were observed between the various catheters, with the silicone-based materials generally exhibiting the greatest ease of removal. Similarly, the catheters exhibited a range of advancing and receding contact angles that were dependent on the chemical nature of each catheter. Finally, whilst the microrugosities of the various catheters differed, no specific relationship to the chemical nature of the biomaterial was apparent. Using multiple regression analysis, the relationship between ease of removal, receding contact angle and surface roughness was defined as: Work done (N mm) 17.18 + 0.055 Rugosity (nm)-0.52 Receding contact angle (degrees) (r = 0.49). Interestingly, whilst the relationship between ease of removal and surface roughness was significant (r = 0.48, p = 0.0005), in which catheter lubricity increased as the surface roughness decreased, this was not the case with the relationship between ease of removal and receding contact angle (r = -0.18, p > 0.05). This study has therefore uniquely defined the contributions of each of these surface properties to catheter lubricity. Accordingly, in the design of urethral catheters. it is recommended that due consideration should be directed towards biomaterial surface roughness to ensure maximal ease of catheter removal. Furthermore, using the method described in this study, differences in the lubricity of the various catheters were observed that may be apparent in their clinical use. (C) 2003 Elsevier Ltd. All rights reserved.

Multivariate Statistical Analysis Applied to an IL6 Signal Transduction Model in Hepatocytes

This paper introduces the application of linear multivariate statistical techniques, including partial least squares (PLS), canonical correlation analysis (CCA) and reduced rank regression (RRR), into the area of Systems Biology. This new approach aims to extract the important proteins embedded in complex signal transduction pathway models.The analysis is performed on a model of intracellular signalling along the janus-associated kinases/signal transducers and transcription factors (JAK/STAT) and mitogen activated protein kinases (MAPK) signal transduction pathways in interleukin-6 (IL6) stimulated hepatocytes, which produce signal transducer and activator of transcription factor 3 (STAT3).A region of redundancy within the MAPK pathway that does not affect the STAT3 transcription was identified using CCA. This is the core finding of this analysis and cannot be obtained by inspecting the model by eye. In addition, RRR was found to isolate terms that do not significantly contribute to changes in protein concentrations, while the application of PLS does not provide such a detailed picture by virtue of its construction.This analysis has a similar objective to conventional model reduction techniques with the advantage of maintaining the meaning of the states prior to and after the reduction process. A significant model reduction is performed, with a marginal loss in accuracy, offering a more concise model while maintaining the main influencing factors on the STAT3 transcription.The findings offer a deeper understanding of the reaction terms involved, confirm the relevance of several proteins to the production of Acute Phase Proteins and complement existing findings regarding cross-talk between the two signalling pathways.

Long-term and Short-term Forecast Models for Poaceae (Grass) Pollen in Poznan, Poland, Constructed Using Regression Analysis

Airborne concentrations of Poaceae pollen have been monitored in Poznań for more than ten years and the length of the dataset is now considered sufficient for statistical analysis. The objective of this paper is to produce long-range forecasts that predict certain characteristics of the grass pollen season (such as the start, peak and end dates of the grass pollen season) as well as short-term forecasts that predict daily variations in grass pollen counts for the next day or next few days throughout the main grass pollen season. The method of forecasting was regression analysis. Correlation analysis was used to examine the relationship between grass pollen counts and the factors that affect its production, release and dispersal. The models were constructed with data from 1994-2004 and tested on data from 2005 and 2006. The forecast models predicted the start of the grass pollen season to within 2 days and achieved 61% and 70% accuracy on a scale of 1-4 when forecasting variations in daily grass pollen counts in 2005 and 2006 respectively. This study has emphasised how important the weather during the few weeks or months preceding pollination is to grass pollen production, and draws attention to the importance of considering large-scale patterns of climate variability (indices of the North Atlantic Oscillation) when constructing forecast models for allergenic pollen.

Problems of allometric scaling analysis: examples from mammalian reproductive biology.

Biological scaling analyses employing the widely used bivariate allometric model are beset by at least four interacting problems: (1) choice of an appropriate best-fit line with due attention to the influence of outliers; (2) objective recognition of divergent subsets in the data (allometric grades); (3) potential restrictions on statistical independence resulting from phylogenetic inertia; and (4) the need for extreme caution in inferring causation from correlation. A new non-parametric line-fitting technique has been developed that eliminates requirements for normality of distribution, greatly reduces the influence of outliers and permits objective recognition of grade shifts in substantial datasets. This technique is applied in scaling analyses of mammalian gestation periods and of neonatal body mass in primates. These analyses feed into a re-examination, conducted with partial correlation analysis, of the maternal energy hypothesis relating to mammalian brain evolution, which suggests links between body size and brain size in neonates and adults, gestation period and basal metabolic rate. Much has been made of the potential problem of phylogenetic inertia as a confounding factor in scaling analyses. However, this problem may be less severe than suspected earlier because nested analyses of variance conducted on residual variation (rather than on raw values) reveals that there is considerable variance at low taxonomic levels. In fact, limited divergence in body size between closely related species is one of the prime examples of phylogenetic inertia. One common approach to eliminating perceived problems of phylogenetic inertia in allometric analyses has been calculation of 'independent contrast values'. It is demonstrated that the reasoning behind this approach is flawed in several ways. Calculation of contrast values for closely related species of similar body size is, in fact, highly questionable, particularly when there are major deviations from the best-fit line for the scaling relationship under scrutiny.

Estimating the Tobit Model with Serial Correlation: an Operational Approach

Several Authors Have Discussed Recently the Limited Dependent Variable Regression Model with Serial Correlation Between Residuals. the Pseudo-Maximum Likelihood Estimators Obtained by Ignoring Serial Correlation Altogether, Have Been Shown to Be Consistent. We Present Alternative Pseudo-Maximum Likelihood Estimators Which Are Obtained by Ignoring Serial Correlation Only Selectively. Monte Carlo Experiments on a Model with First Order Serial Correlation Suggest That Our Alternative Estimators Have Substantially Lower Mean-Squared Errors in Medium Size and Small Samples, Especially When the Serial Correlation Coefficient Is High. the Same Experiments Also Suggest That the True Level of the Confidence Intervals Established with Our Estimators by Assuming Asymptotic Normality, Is Somewhat Lower Than the Intended Level. Although the Paper Focuses on Models with Only First Order Serial Correlation, the Generalization of the Proposed Approach to Serial Correlation of Higher Order Is Also Discussed Briefly.

Multilevel Analysis of Trunk Surface Measurements for Noninvasive Assessment of Scoliosis Deformities.

Study Design. Reliability study. Objectives. To assess between-acquisition reliability of new multilevel trunk cross sections measurements, in order to define what is a real change when comparing 2 trunk surface acquisitions of a same patient, before and after surgery or throughout the clinical monitoring. Summary of Background Data. Several cross-sectional surface measurements have been proposed in the literature for noninvasive assessment of trunk deformity in patients with adolescent idiopathic scoliosis (AIS). However, only the maximum values along the trunk are evaluated and used for monitoring progression and assessing treatment outcome. Methods. Back surface rotation (BSR), trunk rotation (TR), and coronal and sagittal trunk deviation are computed on 300 cross sections of the trunk. Each set of 300 measures is represented as a single functional data, using a set of basis functions. To evaluate between-acquisition variability at all trunk levels, a test-retest reliability study is conducted on 35 patients with AIS. A functional correlation analysis is also carried out to evaluate any redundancy between the measurements. Results. Each set of 300 measures was successfully described using only 10 basis functions. The test-retest reliability of the functional measurements is good to very good all over the trunk, except above the shoulders level. The typical errors of measurement are between 1.20° and 2.2° for the rotational measures and between 2 and 6 mm for deviation measures. There is a very strong correlation between BSR and TR all over the trunk, a moderate correlation between coronal trunk deviation and both BSR and TR, and no correlation between sagittal trunk deviation and any other measurement. Conclusion. This novel representation of trunk surface measurements allows for a global assessment of trunk surface deformity. Multilevel trunk measurements provide a broader perspective of the trunk deformity and allow a reliable multilevel monitoring during clinical follow-up of patients with AIS and a reliable assessment of the esthetic outcome after surgery.