Modelling football match scoring outcomes using multilevel models

Multilevel modelling technique recognizes the existence of hierarchal structures in the data by allowing for random effects at each level in the hierarchy, thus assessing the variation in the dependent variable at several hierarchical levels simultaneously. Multilevel modelling is becoming an increasingly popular technique for analysing nested data with such popularity accredited to the computational advances in the last two decades. In many sports, including football, the game fixtures are nested within seasons, which in turn are nested within country leagues invoking a multilevel structure in the data. Many gaming companies engage in sport data analysis in a bid to understand the dynamics and patterns of the game. This will assist the gaming company in developing fantasy sport games that will enhance gamer engagement and augment revenue to the company. This paper presents a comprehensive description of two and three level models, which are applied to a real football data set accessed from an online free football betting portal. The aim is to examine the relationship between the number of goals scored during a football match and several game-related predictors. These multilevel models, which assume a Poisson distribution and a logarithmic function, are implemented using the facilities of GLLAMM (Generalized Linear Latent and Mixed Models), which is a subroutine of STATA.peer-reviewe

Investigating the factors which affect the performance of the EM algorithm in Latent class models

Latent class models have been used extensively in market segmentation to divide a total market into market groups of consumers who have relatively similar product needs and preferences. The advantage of these models over traditional clustering techniques lies in simultaneous estimation and segmentation, which is carried out using the EM algorithm. The identification of consumer segments allows target-marketing strategies to be developed. The data comprises the rating responses of 262 respondents to 24 laptop profiles described by four item attributes including the brand, price, random access memory (RAM) and the screen size. Using the facilities of R Studio, two latent class models were fitted by varying the number of clusters from 2 to 3. The parameter estimates obtained from these two latent class models were used to simulate a number of data sets for each cluster solution to be able to conduct a Monte-Carlo study, which investigates factors that have an effect on segment membership and parameter recovery and affect computational effort.peer-reviewe

Individual identity in songbirds: signal representations and metric learning for locating the information in complex corvid calls

Bird calls range from simple tones to rich dynamic multi-harmonic structures. The more complex calls are very poorly understood at present, such as those of the scientifically important corvid family (jackdaws, crows, ravens, etc.). Individual birds can recognise familiar individuals from calls, but where in the signal is this identity encoded? We studied the question by applying a combination of feature representations to a dataset of jackdaw calls, including linear predictive coding (LPC) and adaptive discrete Fourier transform (aDFT). We demonstrate through a classification paradigm that we can strongly outperform a standard spectrogram representation for identifying individuals, and we apply metric learning to determine which time-frequency regions contribute most strongly to robust individual identification. Computational methods can help to direct our search for understanding of these complex biological signals

The prevalence of Helicobacter pylori carrier rates among the healthy blood donors in Hong Kong

A serological assay was employed in this study to assess the Helicobacter pylori carrier rates among the healthy blood donors (all Chinese) in Hong Kong. The commercial kit for detecting anti-H. pylori antibody titres was found to have a sensitivity of 84% and a specificity of 85% by using the histochemistry results as the gold standard. Elevated anti-H. pylori antibody titres were observed in 42.4%, 53.2% and 72.2% of the healthy blood donors of age below 20, 21 to 30 and 31 to 40 years respectively. This indicates a steady rise of H. pylori carrier rates with age. The overall H. pylori prevalence rate was 54.9%. The positivity of H. pylori in teenagers appeared to be double that reported in Western countries. Whether this is related to the younger age of peptic ulcer presentation in Hong Kong compared with Western countries is not known. However, there was no significant difference of the H. pylori rates between males and females of each age group although a male predominance has been well established for peptic ulcer in Hong Kong.published_or_final_versio

Global reconstruction of life-history strategies: a case study using tunas

1. Measuring the demographic parameters of exploited populations is central to predicting their vulnerability and extinction risk. However, current rates of population decline and species loss greatly outpace our ability to empirically monitor all populations that are potentially threatened. 2. The scale of this problem cannot be addressed through additional data collection alone, and therefore it is common practice to conduct population assessments based on surrogate data collected from similar species. However, this approach introduces biases and imprecisions that are difficult to quantify. Recent developments in hierarchical modelling have enabled missing values to be reconstructed based on the correlations between available life-history data, linking similar species based on phylogeny and environmental conditions. 3. However, these methods cannot resolve life-history variability among populations or species that are closely placed spatially or taxonomically. Here, theoretically motivated constraints that align with life-history theory offer a new avenue for addressing this problem. We describe a Bayesian hierarchical approach that combines fragmented, multi-species and multi-population data with established life-history theory, in order to objectively determine similarity between populations based on trait correlations (life-history trade-offs) obtained from model fitting. 4. We reconstruct 59 unobserved life-history parameters for 23 populations of tuna that sustain some of the world’s most valuable fisheries. Testing by cross-validation across different scenarios indicated that life-histories were accurately reconstructed when information was available for other populations of the same species. The reconstruction of several traits was also accurate for species represented by a single population, although credible intervals increased dramatically. 5. Synthesis and applications The described Bayesian hierarchical method provides access to life-history traits that are difficult to measure directly, and reconstructs missing life-history information useful for assessing populations and species that are directly or indirectly affected by human exploitation of natural resources. The method is particularly useful for examining populations that are spatially or taxonomically similar. The reconstructed life-history strategies described for the principal market tunas have immediate application to the world-wide management of tuna fisheries that use the steepness of the stock recruitment relationship to determine population productivity

Modeling survival durations of patients undergoing aortic valve replacement

Survival analysis is a useful statistical tool for problems that deal with survival data. This data is used in order to analyze the predicted duration for a certain event to occur. Initial survival analysis was linked explicitly with events related to death. However, this is no longer the case and nowadays survival analysis is used in almost all research areas to model duration of device failure or relapse duration to drug, smoking and alcohol addiction. This paper presents several approaches to model survival durations of patients undergoing aortic valve replacement. These survival models will be used to relate survival durations for censored data to several pre- and post-operative patient related variables to identify risks factors.peer-reviewe

Meta-analysis to study the promotion effect of protective factors in mental health

Standards for accurate and trustworthy reviews, integration and syntheses of studies that address similar research concerns are critical within the scientific world. Very often, researchers are confronted with massive amounts of results that do not always provide them with conclusive answers. By summarizing and synthesizing results of smaller studies that have inconclusive outcomes, it is more likely to produce more robust and accurate estimates and stronger inferences. In the application, this paper combines the results of six studies carried out in six European countries to investigate the promotion effect of protective factors in mental health, namely social emotional, learning, resilience and prosocial behaviour.peer-reviewe