A bayesian model for mixed responses and skew laten variable to measure HRQoL in children

The aim of the thesi is to formulate a suitable Item Response Theory (IRT) based model to measure HRQoL (as latent variable) using a mixed responses questionnaire and relaxing the hypothesis of normal distributed latent variable. The new model is a combination of two models already presented in literature, that is, a latent trait model for mixed responses and an IRT model for Skew Normal latent variable. It is developed in a Bayesian framework, a Markov chain Monte Carlo procedure is used to generate samples of the posterior distribution of the parameters of interest. The proposed model is test on a questionnaire composed by 5 discrete items and one continuous to measure HRQoL in children, the EQ-5D-Y questionnaire. A large sample of children collected in the schools was used. In comparison with a model for only discrete responses and a model for mixed responses and normal latent variable, the new model has better performances, in term of deviance information criterion (DIC), chain convergences times and precision of the estimates.

The assessment of change in rural landscape and the analysis of the driving forces. Proposal of a spatial model for the rural built environment

The presented study carried out an analysis on rural landscape changes. In particular the study focuses on the understanding of driving forces acting on the rural built environment using a statistical spatial model implemented through GIS techniques. It is well known that the study of landscape changes is essential for a conscious decision making in land planning. From a bibliography review results a general lack of studies dealing with the modeling of rural built environment and hence a theoretical modelling approach for such purpose is needed. The advancement in technology and modernity in building construction and agriculture have gradually changed the rural built environment. In addition, the phenomenon of urbanization of a determined the construction of new volumes that occurred beside abandoned or derelict rural buildings. Consequently there are two types of transformation dynamics affecting mainly the rural built environment that can be observed: the conversion of rural buildings and the increasing of building numbers. It is the specific aim of the presented study to propose a methodology for the development of a spatial model that allows the identification of driving forces that acted on the behaviours of the building allocation. In fact one of the most concerning dynamic nowadays is related to an irrational expansion of buildings sprawl across landscape. The proposed methodology is composed by some conceptual steps that cover different aspects related to the development of a spatial model: the selection of a response variable that better describe the phenomenon under study, the identification of possible driving forces, the sampling methodology concerning the collection of data, the most suitable algorithm to be adopted in relation to statistical theory and method used, the calibration process and evaluation of the model. A different combination of factors in various parts of the territory generated favourable or less favourable conditions for the building allocation and the existence of buildings represents the evidence of such optimum. Conversely the absence of buildings expresses a combination of agents which is not suitable for building allocation. Presence or absence of buildings can be adopted as indicators of such driving conditions, since they represent the expression of the action of driving forces in the land suitability sorting process. The existence of correlation between site selection and hypothetical driving forces, evaluated by means of modeling techniques, provides an evidence of which driving forces are involved in the allocation dynamic and an insight on their level of influence into the process. GIS software by means of spatial analysis tools allows to associate the concept of presence and absence with point futures generating a point process. Presence or absence of buildings at some site locations represent the expression of these driving factors interaction. In case of presences, points represent locations of real existing buildings, conversely absences represent locations were buildings are not existent and so they are generated by a stochastic mechanism. Possible driving forces are selected and the existence of a causal relationship with building allocations is assessed through a spatial model. The adoption of empirical statistical models provides a mechanism for the explanatory variable analysis and for the identification of key driving variables behind the site selection process for new building allocation. The model developed by following the methodology is applied to a case study to test the validity of the methodology. In particular the study area for the testing of the methodology is represented by the New District of Imola characterized by a prevailing agricultural production vocation and were transformation dynamic intensively occurred. The development of the model involved the identification of predictive variables (related to geomorphologic, socio-economic, structural and infrastructural systems of landscape) capable of representing the driving forces responsible for landscape changes.. The calibration of the model is carried out referring to spatial data regarding the periurban and rural area of the study area within the 1975-2005 time period by means of Generalised linear model. The resulting output from the model fit is continuous grid surface where cells assume values ranged from 0 to 1 of probability of building occurrences along the rural and periurban area of the study area. Hence the response variable assesses the changes in the rural built environment occurred in such time interval and is correlated to the selected explanatory variables by means of a generalized linear model using logistic regression. Comparing the probability map obtained from the model to the actual rural building distribution in 2005, the interpretation capability of the model can be evaluated. The proposed model can be also applied to the interpretation of trends which occurred in other study areas, and also referring to different time intervals, depending on the availability of data. The use of suitable data in terms of time, information, and spatial resolution and the costs related to data acquisition, pre-processing, and survey are among the most critical aspects of model implementation. Future in-depth studies can focus on using the proposed model to predict short/medium-range future scenarios for the rural built environment distribution in the study area. In order to predict future scenarios it is necessary to assume that the driving forces do not change and that their levels of influence within the model are not far from those assessed for the time interval used for the calibration.

Non-Linear Pricing in Imperfectly Competitive Markets

This thesis is dedicated to the analysis of non-linear pricing in oligopoly. Non-linear pricing is a fairly predominant practice in most real markets, mostly characterized by some amount of competition. The sophistication of pricing practices has increased in the latest decades due to the technological advances that have allowed companies to gather more and more data on consumers preferences. The first essay of the thesis highlights the main characteristics of oligopolistic non-linear pricing. Non-linear pricing is a special case of price discrimination. The theory of price discrimination has to be modified in presence of oligopoly: in particular, a crucial role is played by the competitive externality that implies that product differentiation is closely related to the possibility of discriminating. The essay reviews the theory of competitive non-linear pricing by starting from its foundations, mechanism design under common agency. The different approaches to model non-linear pricing are then reviewed. In particular, the difference between price and quantity competition is highlighted. Finally, the close link between non-linear pricing and the recent developments in the theory of vertical differentiation is explored. The second essay shows how the effects of non-linear pricing are determined by the relationship between the demand and the technological structure of the market. The chapter focuses on a model in which firms supply a homogeneous product in two different sizes. Information about consumers' reservation prices is incomplete and the production technology is characterized by size economies. The model provides insights on the size of the products that one finds in the market. Four equilibrium regions are identified depending on the relative intensity of size economies with respect to consumers' evaluation of the good. Regions for which the product is supplied in a single unit or in several different sizes or in only a very large one. Both the private and social desirability of non-linear pricing varies across different equilibrium regions. The third essay considers the broadband internet market. Non discriminatory issues seem the core of the recent debate on the opportunity or not of regulating the internet. One of the main questions posed is whether the telecom companies, owning the networks constituting the internet, should be allowed to offer quality-contingent contracts to content providers. The aim of this essay is to analyze the issue through a stylized two-sided market model of the web that highlights the effects of such a discrimination over quality, prices and participation to the internet of providers and final users. An overall welfare comparison is proposed, concluding that the final effects of regulation crucially depend on both the technology and preferences of agents.

Harnessing the power of Drosophila model to obtain new insights on MYC and ABCC1 cellular functions: from neuroblastoma to autism spectrum disorder

MYCN amplification is a genetic hallmark of the childhood tumour neuroblastoma. MYCN-MAX dimers activate the expression of genes promoting cell proliferation. Moreover, MYCN seems to transcriptionally repress cell differentiation even in absence of MAX. We adopted the Drosophila eye as model to investigate the effect of high MYC to MAX expression ratio on cells. We found that dMyc overexpression in eye cell precursors inhibits cell differentiation and induces the ectopic expression of Antennapedia (the wing Hox gene). The further increase of MYC/MAX ratio results in an eye-to-wing homeotic transformation. Notably, dMyc overexpression phenotype is suppressed by low levels of transcriptional co-repressors and MYCN associates to the promoter of Deformed (the eye Hox gene) in proximity to repressive sites. Hence, we envisage that, in presence of high MYC/MAX ratio, the “free MYC” might inhibit Deformed expression, leading in turn to the ectopic expression of Antennapedia. This suggests that MYCN might reinforce its oncogenic role by affecting the physiological homeotic program. Furthermore, poor neuroblastoma outcome associates with a high level of the MRP1 protein, encoded by the ABCC1 gene and known to promote drug efflux in cancer cells. Intriguingly, this correlation persists regardless of chemotherapy and ABCC1 overexpression enhances neuroblastoma cell motility. We found that Drosophila dMRP contributes to the adhesion between the dorsal and ventral epithelia of the wing by inhibiting the function of integrin receptors, well known regulators of cell adhesion and migration. Besides, integrins play a crucial role during synaptogenesis and ABCC1 locus is included in a copy number variable region of the human genome (16p13.11) involved in neuropsychiatric diseases. Interestingly, we found that the altered dMRP/MRP1 level affects nervous system development in Drosophila embryos. These preliminary findings point out novel ABCC1 functions possibly defining ABCC1 contribution to neuroblastoma and to the pathogenicity of 16p13.11 deletion/duplication

Assessing the fit of unidimensional IRT models for binary data under model misspecification

Model misspecification affects the classical test statistics used to assess the fit of the Item Response Theory (IRT) models. Robust tests have been derived under model misspecification, as the Generalized Lagrange Multiplier and Hausman tests, but their use has not been largely explored in the IRT framework. In the first part of the thesis, we introduce the Generalized Lagrange Multiplier test to detect differential item response functioning in IRT models for binary data under model misspecification. By means of a simulation study and a real data analysis, we compare its performance with the classical Lagrange Multiplier test, computed using the Hessian and the cross-product matrix, and the Generalized Jackknife Score test. The power of these tests is computed empirically and asymptotically. The misspecifications considered are local dependence among items and non-normal distribution of the latent variable. The results highlight that, under mild model misspecification, all tests have good performance while, under strong model misspecification, the performance of the tests deteriorates. None of the tests considered show an overall superior performance than the others. In the second part of the thesis, we extend the Generalized Hausman test to detect non-normality of the latent variable distribution. To build the test, we consider a seminonparametric-IRT model, that assumes a more flexible latent variable distribution. By means of a simulation study and two real applications, we compare the performance of the Generalized Hausman test with the M2 limited information goodness-of-fit test and the Likelihood-Ratio test. Additionally, the information criteria are computed. The Generalized Hausman test has a better performance than the Likelihood-Ratio test in terms of Type I error rates and the M2 test in terms of power. The performance of the Generalized Hausman test and the information criteria deteriorates when the sample size is small and with a few items.

Essays in nonlinear pricing and rent seeking

This thesis consists of three self-contained essays on nonlinear pricing and rent-seeking. In the first chapter of the thesis, I provide new theoretical insights about non-linear pricing in monopoly and common agency by combining the principal-agent framework with other-regarding preferences. I introduce a new theoretical model that separately characterizes status-seeker and inequity-averse buyers. I show how the buyer’s optimal choice of quality and market inefficiency change when the buyer has other-regarding preferences. In the second chapter, I find the optimal productive rent-seeking and sabotaging efforts when the prize is endogenous. I show that due to the existence of endogeneity, sabotaging the productive rent-seeking efforts causes sabotaging the endogenous part of the prize, which can affect the rent-seeking efforts. Moreover, I introduce social preferences into my model and characterize symmetric productive rent-seeking and sabotaging efforts. In the last chapter, I propose a new theoretical model regarding information disclosure with Bayesian persuasion in rent-seeking contests when the efforts are productive. I show that under one-sided incomplete information, information disclosure decision depends on both the marginal costs of efforts and the marginal benefit of aggregate exerted effort. I find that since the efforts are productive and add a positive surplus on the fixed rent, my model narrows down the conditions for the information disclosure compared to the exogenous model. Under the two-sided incomplete information case, I observe that there is a non-monotone relationship between optimal effort and posterior beliefs. Thus, it might be difficult to conclude whether a contest organizer should disclose any information to contestants.

Essays on asset pricing

The first chapter provides evidence that aggregate Research and Development (R&D) investment drives a persistent component in productivity growth and that this embodies a risk priced in financial markets. In a semi-endogenous growth model, this component is identified by the R&D in excess of equilibrium levels and can be approximated by the Error Correction Term in the cointegration between R&D and Total Factor Productivity. Empirically, the component results being well defined and it satisfies all key theoretical predictions: it exhibits appropriate persistency, it forecasts productivity growth, and it is associated with a cross-sectional risk premium. CAPM is the most foundational model in financial economics, but is known to empirically underestimate expected returns of low-risk assets and overestimate those with high risk. The second chapter studies how risks omission and funding tightness jointly contribute to explaining this anomaly, with the former affecting the definition of assets’ riskiness and the latter affecting how risk is remunerated. Theoretically, the two effects are shown to counteract each other. Empirically, the spread related to binding leverage constraints is found to be significant at 2% yearly. Nonetheless, average returns of portfolios that exploit this anomaly are found to mostly reflect omitted risks, in contrast to their employment in previous literature. The third chapter studies how ‘sustainability’ of assets affect discount rates, which is intrinsically mediated by the risk profile of the assets themselves. This has implications for the assessment of the sustainability-related spread and for hedging changes in the sustainability concern. This mechanism is tested on the ESG-score dimension for US data, with inconclusive evidence regarding the existence of an ESG-related premium in the first place. Also, the risk profile of the long-short ESG portfolio is not likely to impact the sign of its average returns with respect to the sustainability-spread, for the time being.