Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits

Environmental data usually include measurements, such as water quality data, which fall below detection limits, because of limitations of the instruments or of certain analytical methods used. The fact that some responses are not detected needs to be properly taken into account in statistical analysis of such data. However, it is well-known that it is challenging to analyze a data set with detection limits, and we often have to rely on the traditional parametric methods or simple imputation methods. Distributional assumptions can lead to biased inference and justification of distributions is often not possible when the data are correlated and there is a large proportion of data below detection limits. The extent of bias is usually unknown. To draw valid conclusions and hence provide useful advice for environmental management authorities, it is essential to develop and apply an appropriate statistical methodology. This paper proposes rank-based procedures for analyzing non-normally distributed data collected at different sites over a period of time in the presence of multiple detection limits. To take account of temporal correlations within each site, we propose an optimal linear combination of estimating functions and apply the induced smoothing method to reduce the computational burden. Finally, we apply the proposed method to the water quality data collected at Susquehanna River Basin in United States of America, which dearly demonstrates the advantages of the rank regression models.

Rank regression for analysis of clustered data: A natural induced smoothing approach

We consider rank regression for clustered data analysis and investigate the induced smoothing method for obtaining the asymptotic covariance matrices of the parameter estimators. We prove that the induced estimating functions are asymptotically unbiased and the resulting estimators are strongly consistent and asymptotically normal. The induced smoothing approach provides an effective way for obtaining asymptotic covariance matrices for between- and within-cluster estimators and for a combined estimator to take account of within-cluster correlations. We also carry out extensive simulation studies to assess the performance of different estimators. The proposed methodology is substantially Much faster in computation and more stable in numerical results than the existing methods. We apply the proposed methodology to a dataset from a randomized clinical trial.

Weighted rank regression for clustered data analysis

We consider ranked-based regression models for clustered data analysis. A weighted Wilcoxon rank method is proposed to take account of within-cluster correlations and varying cluster sizes. The asymptotic normality of the resulting estimators is established. A method to estimate covariance of the estimators is also given, which can bypass estimation of the density function. Simulation studies are carried out to compare different estimators for a number of scenarios on the correlation structure, presence/absence of outliers and different correlation values. The proposed methods appear to perform well, in particular, the one incorporating the correlation in the weighting achieves the highest efficiency and robustness against misspecification of correlation structure and outliers. A real example is provided for illustration.

Rank-based regression for analysis of repeated measures

We consider rank-based regression models for repeated measures. To account for possible withinsubject correlations, we decompose the total ranks into between- and within-subject ranks and obtain two different estimators based on between- and within-subject ranks. A simple perturbation method is then introduced to generate bootstrap replicates of the estimating functions and the parameter estimates. This provides a convenient way for combining the corresponding two types of estimating function for more efficient estimation.

Induced smoothing for rank regression with censored survival times

Adaptions of weighted rank regression to the accelerated failure time model for censored survival data have been successful in yielding asymptotically normal estimates and flexible weighting schemes to increase statistical efficiencies. However, for only one simple weighting scheme, Gehan or Wilcoxon weights, are estimating equations guaranteed to be monotone in parameter components, and even in this case are step functions, requiring the equivalent of linear programming for computation. The lack of smoothness makes standard error or covariance matrix estimation even more difficult. An induced smoothing technique overcame these difficulties in various problems involving monotone but pure jump estimating equations, including conventional rank regression. The present paper applies induced smoothing to the Gehan-Wilcoxon weighted rank regression for the accelerated failure time model, for the more difficult case of survival time data subject to censoring, where the inapplicability of permutation arguments necessitates a new method of estimating null variance of estimating functions. Smooth monotone parameter estimation and rapid, reliable standard error or covariance matrix estimation is obtained.

Standard errors and covariance matrices for smoothed rank estimators

A 'pseudo-Bayesian' interpretation of standard errors yields a natural induced smoothing of statistical estimating functions. When applied to rank estimation, the lack of smoothness which prevents standard error estimation is remedied. Efficiency and robustness are preserved, while the smoothed estimation has excellent computational properties. In particular, convergence of the iterative equation for standard error is fast, and standard error calculation becomes asymptotically a one-step procedure. This property also extends to covariance matrix calculation for rank estimates in multi-parameter problems. Examples, and some simple explanations, are given.

Closed-form solutions for low-rank non-rigid reconstruction

Recovering the motion of a non-rigid body from a set of monocular images permits the analysis of dynamic scenes in uncontrolled environments. However, the extension of factorisation algorithms for rigid structure from motion to the low-rank non-rigid case has proved challenging. This stems from the comparatively hard problem of finding a linear “corrective transform” which recovers the projection and structure matrices from an ambiguous factorisation. We elucidate that this greater difficulty is due to the need to find multiple solutions to a non-trivial problem, casting a number of previous approaches as alleviating this issue by either a) introducing constraints on the basis, making the problems nonidentical, or b) incorporating heuristics to encourage a diverse set of solutions, making the problems inter-dependent. While it has previously been recognised that finding a single solution to this problem is sufficient to estimate cameras, we show that it is possible to bootstrap this partial solution to find the complete transform in closed-form. However, we acknowledge that our method minimises an algebraic error and is thus inherently sensitive to deviation from the low-rank model. We compare our closed-form solution for non-rigid structure with known cameras to the closed-form solution of Dai et al. [1], which we find to produce only coplanar reconstructions. We therefore make the recommendation that 3D reconstruction error always be measured relative to a trivial reconstruction such as a planar one.

The effect of a self-development coaching program on the psychological health and the academic performance among medical and dental students in Saudi Arabia

Observational studies have shown that medical and dental students have poor psychological health worldwide; however, few interventional studies have been used to test approaches to help students. This thesis used a randomised control trial study design to evaluate the effect of a self-development coaching program on psychological health and the academic performance among medical and dental students in Saudi Arabia. The outcomes indicated that these medical and dental students in Saudi Arabia experienced high levels of depression, anxiety and stress, and that the self-development coaching program was a promising intervention to improve students' psychological health.

Teacher Education, School Effectiveness and Improvement : A Study of Academic and Professional Qualification on Teachers' Job Effectiveness in Nigerian Secondary Schools

This academic work begins with a compact presentation of the general background to the study, which also includes an autobiography for the interest in this research. The presentation provides readers who know little of the topic of this research and of the structure of the educational system as well as of the value given to education in Nigeria. It further concentrates on the dynamic interplay of the effect of academic and professional qualification and teachers' job effectiveness in secondary schools in Nigeria in particular, and in Africa in general. The aim of this study is to produce a systematic analysis and rich theoretical and empirical description of teachers' teaching competencies. The theoretical part comprises a comprehensive literature review that focuses on research conducted in the areas of academic and professional qualification and teachers' job effectiveness, teaching competencies, and the role of teacher education with particular emphasis on school effectiveness and improvement. This research benefits greatly from the functionalist conception of education, which is built upon two emphases: the application of the scientific method to the objective social world, and the use of an analogy between the individual 'organism' and 'society'. To this end, it offers us an opportunity to define terms systematically and to view problems as always being interrelated with other components of society. The empirical part involves describing and interpreting what educational objectives can be achieved with the help of teachers' teaching competencies in close connection to educational planning, teacher training and development, and achieving them without waste. The data used in this study were collected between 2002 and 2003 from teachers, principals, supervisors of education from the Ministry of Education and Post Primary Schools Board in the Rivers State of Nigeria (N=300). The data were collected from interviews, documents, observation, and questionnaires and were analyzed using both qualitative and quantitative methods to strengthen the validity of the findings. The data collected were analyzed to answer the specific research questions and hypotheses posited in this study. The data analysis involved the use of multiple statistical procedures: Percentages Mean Point Value, T-test of Significance, One-Way Analysis of Variance (ANOVA), and Cross Tabulation. The results obtained from the data analysis show that teachers require professional knowledge and professional teaching skills, as well as a broad base of general knowledge (e.g., morality, service, cultural capital, institutional survey). Above all, in order to carry out instructional processes effectively, teachers should be both academically and professionally trained. This study revealed that teachers are not however expected to have an extraordinary memory, but rather looked upon as persons capable of thinking in the right direction. This study may provide a solution to the problem of teacher education and school effectiveness in Nigeria. For this reason, I offer this treatise to anyone seriously committed in improving schools in developing countries in general and in Nigeria in particular to improve the lives of all its citizens. In particular, I write this to encourage educational planners, education policy makers, curriculum developers, principals, teachers, and students of education interested in empirical information and methods to conceptualize the issue this study has raised and to provide them with useful suggestions to help them improve secondary schooling in Nigeria. Though, multiple audiences exist for any text. For this reason, I trust that the academic community will find this piece of work a useful addition to the existing literature on school effectiveness and school improvement. Through integrating concepts from a number of disciplines, I aim to describe as holistic a representation as space could allow of the components of school effectiveness and quality improvement. A new perspective on teachers' professional competencies, which not only take into consideration the unique characteristics of the variables used in this study, but also recommend their environmental and cultural derivation. In addition, researchers should focus their attention on the ways in which both professional and non-professional teachers construct and apply their methodological competencies, such as their grouping procedures and behaviors to the schooling of students. Keywords: Professional Training, Academic Training, Professionally Qualified, Academically Qualified, Professional Qualification, Academic Qualification, Job Effectiveness, Job Efficiency, Educational Planning, Teacher Training and Development, Nigeria.

Is a Majority Enough? Psychological Well-being and Its Relation to Academic and Prosocial Motivation, Self-regulation and Achievement at school

This cross-sectional study analyzed psychological well-being at school using the Self-Determination theory as a theoretical frame-work. The study explored basic psychological needs fulfillment (BPNS), academic (SRQ-A), prosocial self-regulation (SRQ-P) and motivation, and their relationship with achievement in general, special and selective education (N=786, 444 boys, 345 girls, mean age 12 yrs 8 mths). Motivation starts behavior which becomes guided by self-regulation. The perceived locus of control (PLOC) affects how self-determined this behavior will be; in other words, to what extent it is autonomously regulated. In order learn and thus to be able to accept external goals, a student has to feel emotionally safe and have sufficient ego-flexibility—all of which builds on satisfied psychological needs. In this study those conditions were explored. In addition to traditional methods Self-organizing maps (SOM), was used in order to cluster the students according to their well-being, self-regulation, motivation and achievement scores. The main impacts of this research were: a presentation of the theory based alternative of studying psychological well-being at school and usage of both the variable and person-oriented approach. In this Finnish sample the results showed that the majority of students felt well, but the well-being varied by group. Overall about for 11–15% the basic needs were deprived depending on the educational group. Age and educational group were the most effective factors; gender was important in relation to prosocial identified behavior. Although the person-oriented SOM-approach, was in a large extent confirming what was no-ticed by using comparison of the variables: the SEN groups had lower levels of basic needs fulfillment and less autonomous self-regulation, interesting deviations of that rule appeared. Some of the SEL- and GEN-group members ended up in the more unfavorable SOM-clusters, and not all SEN-group members belonged to the poorest clusters (although not to the best either). This evidence refines the well-being and self-regulation picture, and may re-direct intervention plans, and turn our focus also on students who might otherwise remain unnoticed. On the other hand, these results imply simultaneously that in special education groups the average is not the whole truth. On the basis of theoretical and empirical considerations an intervention model was sug-gested. The aim of the model was to shift amotivation or external motivation in a more intrinsic direction. According to the theoretical and empirical evidence this can be achieved first by studying the self-concept a student has, and then trying to affect both inner and environmental factors—including a consideration of the basic psychological needs. Keywords: academic self-regulation, prosocial self-regulation, basic psychological needs, moti-vation, achievement

Cervical headgear in Class II division 1 correction in children : Academic Dissertation

Class II division 1 malocclusion occurs in 3.5 to 13 percent of 7 12 year-old children. It is the most common reason for orthodontic treatment in Finland. Correction is most commonly performed using headgear treatment. The aim of this study was to investigate the effects of cervical headgear treatment on dentition, facial skeletal and soft tissue growth, and upper airway structure, in children. 65 schoolchildren, 36 boys and 29 girls were studied. At the onset of treatment a mean age was 9.3 (range 6.6 12.4) years. All the children were consequently referred to an orthodontist because of Class II division 1 malocclusion. The included children had protrusive maxilla and an overjet of more than 2mm (3 to 11 mm). The children were treated with a Kloehn-type cervical headgear as the only appliance until Class I first molar relationships were achieved. The essential features of the headgear were cervical strong pulling forces, a long upward bent outer bow, and an expanded inner bow. Dental casts and lateral and posteroanterior cephalograms were taken before and after the treatment. The results were compared to a historical, cross-sectional Finnish cohort or to historical, age- and sex-matched normal Class I controls. The Class I first molar relationships were achieved in all the treated children. The mean treatment time was 1.7 (range 0.3-3.1) years. Phase 2 treatments were needed in 52% of the children, most often because of excess overjet or overbite. The treatment decreased maxillary protrusion by inhibiting alveolar forward growth, while the rest of the maxilla and mandible followed normal growth. The palate rotated anteriorly downward. The expansion of the inner bow of the headgear induced widening of the maxilla, nasal cavity, and the upper and lower dental arches. Class II malocclusion was associated with narrower oro- and hypopharyngeal space than in the Class I normal controls. The treatment increased the retropalatal airway space, while the rest of the airway remained unaffected. The facial profile improved esthetically, while the facial convexity decreased. Facial soft tissues masked the facial skeletal convexity, and the soft tissue changes were smaller than skeletal changes. In conclusion, the headgear treatment with the expanded inner bow may be used as an easy and simple method for Class II correction in growing children.

The first cut is the deepest: Repeated interactions of co-authorship and academic productivity in Nobel laureate teams

Despite much in-depth investigation of factors influencing the co-authorship evolution in various scientific fields, our knowledge about how efficiency or creativity is linked to the longevity of collaborative relationships remains very limited. We explore what Nobel laureates’ co-authorship patterns reveal about the nature of scientific collaborations looking at the intensity and success of scientific collaborations across fields and across laureates’ collaborative lifecycles in physics, chemistry, and physiology/medicine. We find that more collaboration with the same researcher is actually no better for advancing creativity: publications produced early in a sequence of repeated collaborations with a given coauthor tend to be published better and cited more than papers that come later in the collaboration with the same coauthor. Our results indicate that scientific collaboration involves conceptual complementarities that may erode over a sequence of repeated interactions.

Transitioning from university to the workplace: Stakeholder perceptions of academic and professional writing demands

This study provides a detailed insight into the changing writing demands from the last year of university study to the first year in the workforce of engineering and accounting professionals. The study relates these to the demands of the writing component of IELTS, which is increasingly used for exit testing. The number of international and local students whose first language is not English and who are studying in English-medium universities has increased significantly in the past decade. Many of these students aim to start working in the country they studied in; however, some employers have suggested that graduates seeking employment have insufficient language skills. This study provides a detailed insight into the changing writing demands from the last year of university study to the first year in the workforce of engineering and accounting professionals (our two case study professions). It relates these to the demands of the writing component of IELTS, which is increasingly used for exit or professional entry testing, although not expressly designed for this purpose. Data include interviews with final year students, lecturers, employers and new graduates in their first few years in the workforce, as well as professional board members. Employers also reviewed both final year assignments, as well as IELTS writing samples at different levels. Most stakeholders agreed that graduates entering the workforce are underprepared for the writing demands in their professions. When compared with the university writing tasks, the workplace writing expected of new graduates was perceived as different in terms of genre, the tailoring of a text for a specific audience, and processes of review and editing involved. Stakeholders expressed a range of views on the suitability of the use of academic proficiency tests (such as IELTS) as university exit tests and for entry into the professions. With regard to IELTS, while some saw the relevance of the two writing tasks, particularly in relation to academic writing, others questioned the extent to which two timed tasks representing limited genres could elicit a representative sample of the professional writing required, particularly in the context of engineering. The findings are discussed in relation to different test purposes, the intersection between academic and specific purpose testing and the role of domain experts in test validation.

Rank-Augmented LU-Algorithm for Computing Generalized Matrix Inverses

A rank-augmnented LU-algorithm is suggested for computing a generalized inverse of a matrix. Initially suitable diagonal corrections are introduced in (the symmetrized form of) the given matrix to facilitate decomposition; a backward-correction scheme then yields a desired generalized inverse.