Notes on the Solution of Dynamic Optimization Problems with Explicit Controls in Discrete-Time Economic Models

Йордан Йорданов, Андрей Василев - В работата се изследват методи за решаването на задачи на оптималното управление в дискретно време с безкраен хоризонт и явни управления. Дадена е обосновка на една процедура за решаване на такива задачи, базирана на множители на Лагранж, коята често се употребява в икономическата литература. Извеждени са необходимите условия за оптималност на базата на уравнения на Белман и са приведени достатъчни условия за оптималност при допускания, които често се използват в икономиката.

Can psychometric testing be used to explain, predict and measure behavioural competences and the funding resource orchestration of the entrepreneur?

Financing is a critical entrepreneurial activity (Shane et al. 2003) and within the study of entrepreneurship, behaviour has been identified as an area requiring further exploration (Bird et al. 2012). Since 2008 supply side conditions for SMEs have been severe and increasingly entrepreneurs have to bundle or ‘orchestrate’ funding from a variety of sources in order to successfully finance the firm (Wright and Stigliani 2013: p.15). This longitudinal study uses psychometric testing to measure the behavioural competences of a panel of sixty entrepreneurs in the Creative Industries sector. Interviews were conducted over a 3 year period to identify finance finding behaviour. The research takes a pragmatic realism perspective to examine process and the different behavioural competences of entrepreneurs. The predictive qualities of this behaviour are explored in a funding context. The research confirmed a strong behavioural characteristic as validated through interviews and psychometric testing, was an orientation towards engagement and working with other organisations. In a funding context, this manifested itself in entrepreneurs using networks, seeking advice and sharing equity to fund growth. These co-operative, collaborative characteristics are different to the classic image of the entrepreneur as a risk-taker or extrovert. Leadership and achievement orientation were amongst the lowest scores. Three distinctive groups were identified and also shown by subsequent analysis to be a positive contribution to how entrepreneurial behavioural competences can be considered. Belonging to one of these three clusters is a strong predictive indicator of entrepreneurial behaviour – in this context, how entrepreneurs access finance. These Clusters were also proven to have different characteristics in relation to funding outcomes. The study seeks to make a contribution through the development of a methodology for entrepreneurs, policy makers and financial institutions to identify competencies in finding finance and overcome problems in information asymmetry.

Salient elements in novice solutions to code writing problems

This paper presents an approach to the evaluation of novice programmers' solutions to code writing problems. The first step was the development a framework comprised of the salient elements, or programming constructs, used in a set of student solutions to three typical code writing assessment problems. This framework was then refined to provide a code quality factor framework that was compared with an analysis using the SOLO taxonomy. We found that combining our framework with the SOLO taxonomy helped to define the SOLO categories and provided an improved approach to applying the principles of SOLO to code writing problems. © 2011, Australian Computer Society, Inc.

A Study of the Behavioural and Affective Profile of Inpatient Adolescent Girls Presenting a Restrictive Anorexia Nervosa

Essai doctoral d'intégration présenté à la Faculté des Études Supérieures et Postdoctorales en vue de l'obtention du grade de Docteur en psychologie (D.Psy.), en psychologie clinique

The ecocultural context and child behavior problems: A qualitative analysis in rural Nepal.

Commonly used paradigms for studying child psychopathology emphasize individual-level factors and often neglect the role of context in shaping risk and protective factors among children, families, and communities. To address this gap, we evaluated influences of ecocultural contextual factors on definitions, development of, and responses to child behavior problems and examined how contextual knowledge can inform culturally responsive interventions. We drew on Super and Harkness' "developmental niche" framework to evaluate the influences of physical and social settings, childcare customs and practices, and parental ethnotheories on the definitions, development of, and responses to child behavior problems in a community in rural Nepal. Data were collected between February and October 2014 through in-depth interviews with a purposive sampling strategy targeting parents (N = 10), teachers (N = 6), and community leaders (N = 8) familiar with child-rearing. Results were supplemented by focus group discussions with children (N = 9) and teachers (N = 8), pile-sort interviews with mothers (N = 8) of school-aged children, and direct observations in homes, schools, and community spaces. Behavior problems were largely defined in light of parents' socialization goals and role expectations for children. Certain physical settings and times were seen to carry greater risk for problematic behavior when children were unsupervised. Parents and other adults attempted to mitigate behavior problems by supervising them and their social interactions, providing for their physical needs, educating them, and through a shared verbal reminding strategy (samjhaune). The findings of our study illustrate the transactional nature of behavior problem development that involves context-specific goals, roles, and concerns that are likely to affect adults' interpretations and responses to children's behavior. Ultimately, employing a developmental niche framework will elucidate setting-specific risk and protective factors for culturally compelling intervention strategies.

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

A Study of the Behavioural and Affective Profile of Inpatient Adolescent Girls Presenting a Restrictive Anorexia Nervosa

Essai doctoral d'intégration présenté à la Faculté des Études Supérieures et Postdoctorales en vue de l'obtention du grade de Docteur en psychologie (D.Psy.), en psychologie clinique

Scale-dependent to scale-free: daily behavioural switching and optimized searching in a marine predator

Highlights • We study diel behavioural differences in activity patterns in bigeye tuna. • Daytime activity patterns showed scale free movements consistent with searching. • Night-time activity showed simpler movements indicative of rich patch exploitation. • The results confirm predictions of the Lévy foraging hypothesis.

Scale-dependent to scale-free: daily behavioural switching and optimized searching in a marine predator

Highlights • We study diel behavioural differences in activity patterns in bigeye tuna. • Daytime activity patterns showed scale free movements consistent with searching. • Night-time activity showed simpler movements indicative of rich patch exploitation. • The results confirm predictions of the Lévy foraging hypothesis.

Optimal Scaffolding Methods for Students with Emotional and Behavioural Disorders

Optimal assistance of an adult, adapted to the current level of understanding of the student (scaffolding), can help students with emotional and behavioural problems (EBD) to demonstrate a similar level of understanding on scientific tasks, compared to students from regular education (Van Der Steen, Steenbeek, Wielinski & Van Geert, 2012). In the present study the optimal scaffolding techniques for EBD students were investigated, as well as how these differ from scaffolding techniques used for regular students. A researcher visited five EBD students and five regular students (aged three to six years old) three times in a 1,5 years period. Student and researcher worked together on scientific tasks about gravity and air pressure, while the researcher asked questions. An adaptive protocol was used, so that all children were asked the same basic questions about the mechanisms of the task. Beside this, the researcher was also allowed to ask follow-up questions and use scaffolding methods when these seemed necessary. We found a bigger amount of scaffolding in the group of EBD students compared to the regular students. The scaffolding techniques that were used also differed between the two groups. For EBD students, we saw more scaffolding strategies focused on keeping the student committed to the task, and less strategies aimed at the relationship between the child and the researcher. Furthermore, in the group of regular students we saw a decreasing trend in the amount of scaffolding over the course of three visits. This trend was not visible for the EBD students. These results highlight the importance for using different scaffolding strategies when working with EBD students compared to regular students. Future research can give a clearer image of the differences in scaffolding needs between these two groups.

Naming the pain in requirements engineering: Contemporary problems, causes, and effects in practice

Requirements Engineering (RE) has received much attention in research and practice due to its importance to software project success. Its inter-disciplinary nature, the dependency to the customer, and its inherent uncertainty still render the discipline diffcult to investigate. This results in a lack of empirical data. These are necessary, however, to demonstrate which practically relevant RE problems exist and to what extent they matter. Motivated by this situation, we initiated the Naming the Pain in Requirements Engineering (NaPiRE) initiative which constitutes a globally distributed, bi-yearly replicated family of surveys on the status quo and problems in practical RE.

In this article, we report on the analysis of data obtained from 228 companies in 10 countries. We apply Grounded Theory to the data obtained from NaPiRE and reveal which contemporary problems practitioners encounter. To this end, we analyse 21 problems derived from the literature with respect to their relevance and criticality in dependency to their context, and we complement this picture with a cause-effect analysis showing the causes and effects surrounding the most critical problems.

Our results give us a better understanding of which problems exist and how they manifest themselves in practical environments. Thus, we provide a rst step to ground contributions to RE on empirical observations which, by now, were dominated by conventional wisdom only.

EXAMINING THE IMPACT OF TEACHER PRACTICE ON STUDENT LEARNING IN INTERACTIVE RADIO INSTRUCTION (IRI) CLASSROOMS: LESSONS FROM NEW DELHI AND RAJASTHAN (INDIA)

This quantitative study examines the impact of teacher practices on student achievement in classrooms where the English is Fun Interactive Radio Instruction (IRI) programs were being used. A contemporary IRI design using a dual-audience approach, the English is Fun IRI programs delivered daily English language instruction to students in grades 1 and 2 in Delhi and Rajasthan through 120 30-minute programs via broadcast radio (the first audience) while modeling pedagogical techniques and behaviors for their teachers (the second audience). Few studies have examined how the dual-audience approach influences student learning. Using existing data from 32 teachers and 696 students, this study utilizes a multivariate multilevel model to examine the role of the primary expectations for teachers (e.g., setting up the IRI classroom, following instructions from the radio characters and ensuring students are participating) and the role of secondary expectations for teachers (e.g., modeling pedagogies and facilitating learning beyond the instructions) in promoting students’ learning in English listening skills, knowledge of vocabulary and use of sentences. The study finds that teacher practice on both sets of expectations mattered, but that practice in the secondary expectations mattered more. As expected, students made the smallest gains in the most difficult linguistic task (sentence use). The extent to which teachers satisfied the primary and secondary expectations was associated with gains in all three skills – confirming the relationship between students’ English proficiency and teacher practice in a dual-audience program. When it came to gains in students’ scores in sentence use, a teacher whose focus was greater on primary expectations had a negative effect on student performance in both states. In all, teacher practice clearly mattered but not in the same way for all three skills. An optimal scenario for teacher practice is presented in which gains in all three skills are maximized. These findings have important implications for the way the classroom teacher is cast in IRI programs that utilize a dual-audience approach and in the way IRI programs are contracted insofar as the role of the teacher in instruction is minimized and access is limited to instructional support from the IRI lessons alone.

Case-Based Reasoning as a Heuristic Selector in a Hyper-Heuristic for Course Timetabling Problems

This paper studies Knowledge Discovery (KD) using Tabu Search and Hill Climbing within Case-Based Reasoning (CBR) as a hyper-heuristic method for course timetabling problems. The aim of the hyper-heuristic is to choose the best heuristic(s) for given timetabling problems according to the knowledge stored in the case base. KD in CBR is a 2-stage iterative process on both case representation and the case base. Experimental results are analysed and related research issues for future work are discussed.

Indoor air quality in Portuguese schools: levels and sources of pollutants

Indoor air quality (IAQ) parameters in 73 primary classrooms in Porto were examined for the purpose of assessing levels of volatile organic compounds (VOCs), aldehydes, particulate matter, ventilation rates and bioaerosols within and between schools, and potential sources. Levels of VOCs, aldehydes, PM2.5 , PM10 , bacteria and fungi, carbon dioxide (CO2 ), carbon monoxide, temperature and relative humidity were measured indoors and outdoors and a walkthrough survey was performed concurrently. Ventilation rates were derived from CO2 and occupancy data. Concentrations of CO2 exceeding 1000 ppm were often encountered, indicating poor ventilation. Most VOCs had low concentrations (median of individual species <5 μg/m(3) ) and were below the respective WHO guidelines. Concentrations of particulate matter and culturable bacteria were frequently higher than guidelines/reference values. The variability of VOCs, aldehydes, bioaerosol concentrations, and CO2 levels between schools exceeded the variability within schools. These findings indicate that IAQ problems may persist in classrooms where pollutant sources exist and classrooms are poorly ventilated; source control strategies (related to building location, occupant behavior, maintenance/cleaning activities) are deemed to be the most reliable for the prevention of adverse health consequences in children in schools.