On sample selection models and skew distributions

This thesis is concerned with methods for dealing with missing data in nonrandom samples and recurrent events data. The first part of this thesis is motivated by scores arising from questionnaires which often follow asymmetric distributions, on a fixed range. This can be due to scores clustering at one end of the scale or selective reporting. Sometimes, the scores are further subjected to sample selection resulting in partial observability. Thus, methods based on complete cases for skew data are inadequate for the analysis of such data and a general sample selection model is required. Heckman proposed a full maximum likelihood estimation method under the normality assumption for sample selection problems, and parametric and non-parametric extensions have been proposed. A general selection distribution for a vector Y 2 Rp has a PDF fY given by fY(y) = fY?(y) P(S? 2 CjY? = y) P(S? 2 C) ; where S? 2 Rq and Y? 2 Rp are two random vectors, and C is a measurable subset of Rq. We use this generalization to develop a sample selection model with underlying skew-normal distribution. A link is established between the continuous component of our model log-likelihood function and an extended version of a generalized skewnormal distribution. This link is used to derive the expected value of the model, which extends Heckman's two-step method. The general selection distribution is also used to establish the closed skew-normal distribution as the continuous component of the usual multilevel sample selection models. Finite sample performances of the maximum likelihood estimator of the models are studied via Monte Carlo simulation. The model parameters are more precisely estimated under the new models, even in the presence of moderate to extreme skewness, than the Heckman selection models. Application to data from a study of neck injuries where the responses are substantially skew successfully discriminates between selection and inherent skewness, and the multilevel model is used to analyze jointly unit and item non-response. We also discuss computational and identification issues, and provide an extension of the model using copula-based sample selection models with truncated marginals. The second part of this thesis is motivated by studies that seek to analyze processes that generate events repeatedly over time. We consider the number of events per subject within a specified study period as the primary outcome of interest. One considerable challenge in the analysis of this type of data is the large proportion of patients that might discontinue before the end of the study, leading to partially observed data. Sophisticated sensitivity analyses tools are therefore necessary for the analysis of such data. We propose the use of two frequentist based imputation methods for dealing with missing data in recurrent event data framework. The recurrent events are modeled as over-dispersed Poisson data, with constant rate function. Different assumptions about future behavior of dropouts depending on reasons for dropout and treatment received are made and evaluated in a simulation study. We illustrate our approach with a clinical trial in patients who suffer from bladder cancer

A robust imputation method for missing responses and covariates in sample selection models

Sample selection arises when the outcome of interest is partially observed in a study. Although sophisticated statistical methods in the parametric and non-parametric framework have been proposed to solve this problem, it is yet unclear how to deal with selectively missing covariate data using simple multiple imputation techniques, especially in the absence of exclusion restrictions and deviation from normality. Motivated by the 2003-2004 NHANES data, where previous authors have studied the effect of socio-economic status on blood pressure with missing data on income variable, we proposed the use of a robust imputation technique based on the selection-t sample selection model. The imputation method, which is developed within the frequentist framework, is compared with competing alternatives in a simulation study. The results indicate that the robust alternative is not susceptible to the absence of exclusion restriction- a property inherited from the parent selection-t model- and performs better than models based on the normal assumption even when the data is generated from the normal distribution. Applications to missing outcome and covariate data further corroborate the robustness properties of the pro-posed method. We implemented the proposed approach within the MICE environment in R Statistical Software

On the extended two-parameter generalized skew-normal distribution

We propose a three-parameter skew-normal distribution, obtained by using hidden truncation on a skew-normal random variable. The hidden truncation framework permits direct interpretation of the model parameters. A link is established between the model and the closed skew-normal distribution

Predictive performance of penalized beta regression model for continuous bounded outcomes

Prediction models for continuous bounded outcomes are often developed by fitting ordinary least-square regression. However, predicted values from such method may lie outside the range of the outcome as it is bounded within a fixed range, with nonlinear expectation due to the ceiling and floor effects of the bounds. Thus, regular regression models such as normal linear or nonlinear models, are inadequate for prediction purposes for bounded response variable and the use of distributions that can model different shapes are essential. Beta regression, apart from modeling different shapes and constraining predictions to an admissible range, has been shown to be superior to alternative methods for data fitting but not for prediction purposes. We take data structures into account and compared various penalized beta regression method on predictive accuracy for bounded outcome variables using optimism corrected measures. Contrary to results obtained under many regression contexts, the classical maximum likelihood method produced good predictive accuracy in terms of R2 and RMSE. The ridge penalized beta regression performed better in terms of g-index, which is a measure of performance of the methods in external data sets. We restricted attention to prespecified models throughout and as such variable selection methods are not evaluated

A Sample Selection Model with Skew-normal Distribution

Non-random sampling is a source of bias in empirical research. It is common for the outcomes of interest (e.g. wage distribution) to be skewed in the source population. Sometimes, the outcomes are further subjected to sample selection, which is a type of missing data, resulting in partial observability. Thus, methods based on complete cases for skew data are inadequate for the analysis of such data and a general sample selection model is required. Heckman proposed a full maximum likelihood estimation method under the normality assumption for sample selection problems, and parametric and non-parametric extensions have been proposed. We generalize Heckman selection model to allow for underlying skew-normal distributions. Finite-sample performance of the maximum likelihood estimator of the model is studied via simulation. Applications illustrate the strength of the model in capturing spurious skewness in bounded scores, and in modelling data where logarithm transformation could not mitigate the effect of inherent skewness in the outcome variable

Study on microstructure and mechanical properties of 304 stainless steel joints by TIG-MIG hybrid welding

Abstract: Stainless steel is a family of Fe-based alloys having excellent resistance to corrosion, and as such has been used imperatively for kitchen utensils, transportation, building constructions and much more. This paper presents the work conducted on the material characterizations of a TIG-MIG hybrid welded joint of type 304 austenitic stainless steel. The welding processes were conducted in three phases. The phases of welding employed are MIG welding using a current of 170A, TIG welding using the current of 190A, and a hybrid TIG-MIG welding with currents of 190/170A respectively. The MIG, TIG, and hybrid TIG-MIG weldments were characterized with incomplete penetration, full penetration and excess penetration of weld. Intergranular austenite was created towards the transition zone and the HAZ. The thickness of the delta ferrite (δ-Fe) formed in the microstructures of the TIG weld is more than the thickness emerged in the microstructures of MIG weld and hybrid TIG-MIG welds. A TIG-MIG hybrid weld of specimen welded at the currents of 190/170A has the highest UTS value and percentage elongation of 397.72 MPa and 35.7 %. The TIG-MIG hybrid welding can be recommended for high-tech industrial applications such as nuclear, aircraft, food processing, and automobile industry

Designing and fabrication of an installation PV solar modules tilting platform

The optimum tilt-angle of a fixed photovoltaic solar panel is very important during the installation, in order to best exploit the accessible output power efficiency of the panel. The output power effectiveness of a PV solar collector is profoundly affected by its tilt-angle to the horizontal and its orientation. This is because of the detail that the sun’s angle varies at every point of time and location. The solar photovoltaic tilting platform plays a dynamic role in the installation of the solar photovoltaic panel. From one perspective, it protects the solar panel from mechanical pressures that can arise from the wind movement and the hand; it provides means of adjustment for the solar panel. The proposed solar photovoltaic tilting platform was designed for an adjustable angle capacity oscillating from 0? to 40?; the materials used for the construction of the tilting platform are capable to withstand a load of 45kg and resist a temperature of -50? F to 150? F under a maximum wind force of 3.78N. The numerous mechanisms of the PV tilting platform prototype were tested, the stability, strength, easy titling, and overall performance of the PV tilting platform were declared as satisfactory

Feasibility study of rehabilitation for cardiac patients aided by an artificial intelligence web-based programme: a randomised controlled trial (RECAP trial)—a study protocol

Introduction: Cardiac rehabilitation (CR) delivered by rehabilitation specialists in a healthcare setting is effective in improving functional capacity and reducing readmission rates after cardiac surgery. It is also associated with a reduction in cardiac mortality and recurrent myocardial infarction. This trial assesses the feasibility of a home-based CR programme delivered using a mobile application (app).Methods: The Rehabilitation through Exercise prescription for Cardiac patients using an Artificial intelligence web-based Programme (RECAP) randomised controlled feasibility trial is a single-centre prospective study, in which patients will be allocated on a 1:1 ratio to a home-based CR programme delivered using a mobile app with accelerometers or standard hospital-based rehabilitation classes. The home-based CR programme will employ artificial intelligence to prescribe exercise goals to the participants on a weekly basis. The trial will recruit 70 patients in total. The primary objectives are to evaluate participant recruitment and dropout rates, assess the feasibility of randomisation, determine acceptability to participants and staff, assess the rates of potential outcome measures and determine hospital resource allocation to inform the design of a larger randomised controlled trial for clinical efficacy and health economic evaluation. Secondary objectives include evaluation of health-related quality of life and 6 minute walk distance.Ethics and dissemination: RECAP trial received a favourable outcome from the Berkshire research ethics committee in September 2022 (IRAS 315483)

Rethinking Green Supply Chain Management Practices Impact on Company Performance: A Close-up Insight

Manufacturing organisations have contributed to a poor living environment via unsustainable practices in the production process and the entire service delivery operation. More importantly, the health performance of manufacturing employees may also be affected by unsustainable production practices in the industry. Therefore, the green supply chain management (GSCM) practice has become a topical issue in recent decades due to its significant impact on the ecosystem at large. Via green practices, various performances have been achieved in organisations; meanwhile, the relationships between the practices and performance metrics in most developing countries are unclear, although there have been supposed general submissions. In addition, the study of relationships in a leading business conglomerate in developing nations is rare. Therefore, this paper investigated relationships between GSCM practices and performance metrics in a leading manufacturing organisation in Africa by using a close-up study approach with data collected from 154 respondents. The data were analysed using multiple methods such as factor analysis to consolidate the measured variables; correlation, multiple regression analysis with stepwise estimation, and structural equation modelling (SEM) were used to examine the relationships between GSCM practices and performance. The results of these analyses revealed that environmental performance is significantly predicted by the measure of the organisation’s commitment to GSCM vision, while financial performance is significantly impacted by eco-centric consumption and education. This study concludes that inhouse-drafted strategies based on the insight from the study will facilitate the optimisation of GSCM practice

Effects of a novel, brief psychological therapy (Managing Unusual Sensory Experiences) for hallucinations in first episode psychosis (MUSE FEP): findings from an exploratory randomised controlled trial.

Hallucinations are a common feature of psychosis, yet access to effective psychological treatment is limited. The Managing Unusual Sensory Experiences for First-Episode-Psychosis (MUSE-FEP) trial aimed to establish the feasibility and acceptability of a brief, hallucination-specific, digitally provided treatment, delivered by a non-specialist workforce for people with psychosis. MUSE uses psychoeducation about the causal mechanisms of hallucinations and tailored interventions to help a person understand and manage their experiences. We undertook a two-site, single-blind (rater) Randomised Controlled Trial and recruited 82 participants who were allocated 1:1 to MUSE and treatment as usual (TAU) (n=40) or TAU alone (n=42). Participants completed assessments before and after treatment (2 months), and at follow up (3-4 months). Information on recruitment rates, adherence, and completion of outcome assessments was collected. Analyses focussed on feasibility outcomes and initial estimates of intervention effects to inform a future trial. The trial is registered with the ISRCTN registry 16793301. Criteria for the feasibility of trial methodology and intervention delivery were met. The trial exceeded the recruitment target, had high retention rates (87.8%) at end of treatment, and at follow up (86.6%), with good acceptability of treatment. There were 3 serious adverse events in the therapy group, and 5 in the TAU group. Improvements were evident in both groups at the end of treatment and follow up, with a particular benefit in perceived recovery in the MUSE group. We showed it was feasible to increase access to psychological intervention but a definitive trial requires further changes to the trial design or treatment