Quantile-based methods for prediction, risk measurement and inference

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The focus of this thesis is on the employment of theoretical and practical quantile methods in addressing prediction, risk measurement and inference problems. From a prediction perspective, a problem of creating model-free prediction intervals for a future unobserved value of a random variable drawn from a sample distribution is considered. With the objective of reducing prediction coverage error, two common distribution transformation methods based on the normal and exponential distributions are presented and they are theoretically demonstrated to attain exact and error-free prediction intervals respectively. The second problem studied is that of estimation of expected shortfall via kernel smoothing. The goal here is to introduce methods that will reduce the estimation bias of expected shortfall. To this end, several one-step bias correction expected shortfall estimators are presented and investigated via simulation studies and compared with one-step estimators. The third problem is that of constructing simultaneous confidence bands for quantile regression functions when the predictor variables are constrained within a region is considered. In this context, a method is introduced that makes use of the asymmetric Laplace errors in conjunction with a simulation based algorithm to create confidence bands for quantile and interquantile regression functions. Furthermore, the simulation approach is extended to an ordinary least square framework to build simultaneous bands for quantiles functions of the classical regression model when the model errors are normally distributed and when this assumption is not fulfilled. Finally, attention is directed towards the construction of prediction intervals for realised volatility exploiting an alternative volatility estimator based on the difference of two extreme quantiles. The proposed approach makes use of AR-GARCH procedure in order to model time series of intraday quantiles and forecast intraday returns predictive distribution. Moreover, two simple adaptations of an existing model are also presented

Statistical analysis of birth weight in Awassi sheep in Iraq

In the Awassi flock raised at the Hammam Al-Alil Experiment Station, Mosul University, Iraq, 336 single and 62 twin births were recorded over the years 1968-1972. The environmental factors, year of birth, sex of lamb, age of dam, weight of dam, month and type of birth were assessed with respect to their influence on birth weight, and repeatability of birth weight was estimated. Month of birth, sex of lamb, type of birth and weight of dam were highly significant effects (P \u3c .01). However, in this study, year of birth appeared to have less significant (0.1 \u3c P \u3c 0.25) influence on birth weight of lambs since all lambs were born in the same season of each year and feeding and management were quite similar in all years. The phenotypic correlation between birth weight of lamb and weight of dam was 0.32, and repeatability of birth weight as a trait of the dam was estimated to be 0.23. The results provide a good illustration of the general level of management and environment prevailing in the ewe flock, with birth weight as indicator

The Effect of Using CALLA Instruction Strategies on 9th Grade Students' Writing Achievement and Satisfaction

This study aims to investigate the effect of using CALLA (Cognitive Academic Language Learning Approach (instruction strategies  on 9th grade students' achievement and their satisfaction in learning by these strategies,  and how are they influenced by certain strategies that are used in this research such as: visualizing and selective attention. The study aim is to explore the effect of using CALLA instruction strategies on 9th grade students' writing achievement and satisfaction in Mafraq city.    The researcher used a quasi-experimental design, the participants in this study were assigned randomly into four group: two experimental groups totaling (15) students in each and two control groups totaling (15) students in each. The control groups (30 students) studied the writing traditionally, while the experimental groups (30 students) studied the writing through CALLA. A pre-test was administered to the groups to make sure that there were no significant differences between their performances in writing achievement and satisfaction achievement scale.The findings of the study showed that there are significant statistical differences at the level of (α =0.05) attributed to the method as (f) value totaled 55.395 with a significance of 0.000 in favor of the experimental groups  with no significant statistical differences attributed to gender or interaction between gender and method. Moreover,  there are significant statistical differences at the level of (α =0.05) attributed to the method in wiring achievement satisfaction as (f) value totaled 206.501 with a significance of 0.000 in favor of the experimental groups with no significant statistical differences attributed to gender or interaction between gender and method.Based on the findings of the study the researcher presented several recommendations and implications. Keywords: CALLA Instruction Strategies. Writing. Achievement. Satisfaction. 9th grade.

On the rotating surge and stall and the polar control method

In this paper, the polar controller is applied to the three-state, one-mode Moore-Greitzer Compressor model. A benchmark is first established with a backstepping controller. The polar control method is then explained, and compared to the backstepping controller. The polar controller is used successfully to control the surge and stall problem in the presence of both disturbances and uncertainties

Influence of boundary conditions and anthropogenic emission inventories on simulated O3 and PM2.5 concentrations over Lebanon

AbstractThis study investigates the influence of boundary conditions and anthropogenic emission inventories on the simulated O3 and PM2.5 concentrations over a middle-eastern country – Lebanon. The Polyphemus chemical transport model (CTM) is used over Lebanon to simulate O3 and PM2.5 concentrations. Comparisons to measurements at a sub-urban site of Beirut between 2 and 13 July 2011 show that O3 is largely over-estimated when concentrations from a large-scale model are used as boundary conditions, as used in Waked et al. (2013). A global anthropogenic emission inventory (EDGAR-HTAP) is used with Polyphemus, in order to provide anthropogenic emissions for the Middle-East domain. Over Lebanon, sensitivity to emissions and to boundary conditions have been investigated. The comparison of EDGAR-HTAP to Waked et al. (2012) over Lebanon highlights high discrepancies between the inventories both in terms of emission estimates and spatial distribution. However, when studying the sensitivity to boundary conditions, O3 is well modeled when a Middle-East domain and the Lebanon domain are nested and thus achieves better statistics. The observed concentration is 48.8 μg m−3 and the respective concentrations for the simulation using MOZART4 and the one using the Polyphemus/Middle-East are 154.8 and 65.1 μg m−3. As for PM2.5 which is less sensitive to regional transport than O3, the influence of the boundary conditions on the PM2.5 concentrations at the site of comparison is low. The observed concentration is 20.7 μg m−3, while the modeled concentrations are 20.7 and 20.1 μg m−3 respectively

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented

The Effect of Resveratrol on Swarming Differentiation and the Expression of Some Virulence Factors in Proteus vulgaris

Resveratrol (3,5,4'-trihydroxy-trans-stilbene) is a stilbenoid, a type of natural phenol, and a phytoalexin with anti-inflammatory and antioxidant activities. It is produced naturally by several plants especially the roots of the Japanese Knotweed when under attack by pathogens such as bacteria or fungi. In this study we have verified that resveratrol has activity against Proteus vulgaris, an important pathogen infecting the urinary tract by investigating its effect on swarming and some virulence factor expression(haemolysin and urease).Swarming inhibition was determined on Luria Bertani agar with and without resveratrol and then bacteria was harvested to assay cell length and the production of haemolysin and urease.  Resveratrol significantly inhibited swarming and virulence factor expression but its effect on growth rate was not significant. Keywords: Resveratrol, Proteus vulgaris, phytoalexin, haemolysin, urease