Beyond first-order asymptotics for Cox regression

To go beyond standard first-order asymptotics for Cox regression, we develop parametric bootstrap and second-order methods. In general, computation of $P$-values beyond first order requires more model specification than is required for the likelihood function. It is problematic to specify a censoring mechanism to be taken very seriously in detail, and it appears that conditioning on censoring is not a viable alternative to that. We circumvent this matter by employing a reference censoring model, matching the extent and timing of observed censoring. Our primary proposal is a parametric bootstrap method utilizing this reference censoring model to simulate inferential repetitions of the experiment. It is shown that the most important part of improvement on first-order methods - that pertaining to fitting nuisance parameters - is insensitive to the assumed censoring model. This is supported by numerical comparisons of our proposal to parametric bootstrap methods based on usual random censoring models, which are far more unattractive to implement. As an alternative to our primary proposal, we provide a second-order method requiring less computing effort while providing more insight into the nature of improvement on first-order methods. However, the parametric bootstrap method is more transparent, and hence is our primary proposal. Indications are that first-order partial likelihood methods are usually adequate in practice, so we are not advocating routine use of the proposed methods. It is however useful to see how best to check on first-order approximations, or improve on them, when this is expressly desired.Comment: Published at http://dx.doi.org/10.3150/13-BEJ572 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Effects of Tourism on Venice: Commercial Changes over 30 Years

Tourism is becoming one of the most important economic drivers in the urban context. With this in mind, several cities have tried to adapt their economies to satisfy the demands of the influx of tourism. The main consequences of this trend are the re-shaping of urban areas, with particular regard to art cities. This phenomenon is particularly evident in Venice’s historical city centre. In order to better comprehend the changes that have taken place, we have put together a research based analysis of the commercial structure of the city. Particular attention has been given to comparing and contrasting the retail business over the last thirty years.commercial structure, historical city centre, retail, Venice

Translating Predictive Models for Alzheimer’s Disease to Clinical Practice: User Research, Adoption Opportunities, and Conceptual Design of a Decision Support Tool

Alzheimer’s Disease (AD) is a common form of Dementia with terrible impact on patients, families, and the healthcare sector. Recent computational advances, such as predictive models, have improved AD data collection and analysis, disclosing the progression pattern of the disease. Whilst clinicians currently rely on a qualitative, experience-led approach to make decisions on patients’ care, the Event-Based Model (EBM) has shown promising results for familial and sporadic AD, making it well positioned to inform clinical decision-making. What proves to be challenging is the translation of computational implementations to clinical applications, due to lack of human factors considerations. The aim of this Ph.D. thesis is to (1) explore barriers and opportunities to the adoption of predictive models for AD in clinical practice; and (2) develop and test the design concept of a tool to enable EBM exploitation by AD clinicians. Following a user-centred design approach, I explored current clinical needs and practices, by means of field observations, interviews, and surveys. I framed the technical-clinical gap, identifying the technical features that were better suited for clinical use, and research-oriented clinicians as the best placed to initially adopt the technology. I designed and tested with clinicians a prototype, icompass, and reviewed it with the technical teams through a series of workshops. This approach fostered a thorough understanding of clinical users’ context and perceptions of the tool’s potential. Furthermore, it provided recommendations to computer scientists pushing forward the models and tool’s development, to enhance user relevance in the future. This thesis is one of the few works addressing a lack of consensus on successful adoption and integration of such innovations to the healthcare environment, from a human factors’ perspective. Future developments should improve prototype fidelity, with interleaved clinical testing, refining design, algorithm, and strategies to facilitate the tool’s integration within clinical practice

Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem

We consider the university course timetabling problem, which is one of the most studied problems in educational timetabling. In particular, we focus our attention on the formulation known as the curriculum-based course timetabling problem, which has been tackled by many researchers and for which there are many available benchmarks. The contribution of this paper is twofold. First, we propose an effective and robust single-stage simulated annealing method for solving the problem. Secondly, we design and apply an extensive and statistically-principled methodology for the parameter tuning procedure. The outcome of this analysis is a methodology for modeling the relationship between search method parameters and instance features that allows us to set the parameters for unseen instances on the basis of a simple inspection of the instance itself. Using this methodology, our algorithm, despite its apparent simplicity, has been able to achieve high quality results on a set of popular benchmarks. A final contribution of the paper is a novel set of real-world instances, which could be used as a benchmark for future comparison

The Power Of Instagram In Tourism: Visual Content Categories In Small Destinations

The advent of the web and its networked evolution, has brought to an enormous production of data. Different forms of content such as textual, visual and audio-visual can be posted online but tourism is a sector particularly linked to imaging. Photos have several roles in documenting and shaping the tourist’s experience this is why photo-based social media use in tourism is constantly growing. Following this trend, this research would like to adopt an innovative approach in analyzing the photos of small destinations uploaded on Instagram to understand the categories of the different image contents. Content analysis is applied in order to identify photo categories in small destinations, showing that some adjustments must be done to the ones that were previously applied in literature referring to bigger contexts. The objective is to shed light on how to usefully exploit user generated images to provide both an economic and a social contribution to the tourism industry. In fact, destination management organizations can obtain useful insights for better answering to tourists’ needs and wants.The advent of the web and its evolution have brought to an enormous production of data. Different forms of content such as textual, visual and audio-visual can be posted online but tourism is a sector particularly linked to imaging. Photos have several roles in documenting and shaping the tourist's experience and this is the reason why in the tourism sector photo-based social media use is constantly growing. Following this trend, this research would like to adopt a content analysis to the photos of small destinations uploaded on Instagram, to categorize the different image contents. In the literature this analysis was applied referring to big and famous cities mainly, but since small destinations have different features, more studies about this topic are needed. The objective is to shed light on which contents are uploaded as pictures related to small destinations. A framework for analysing contents can help destination management organizations to obtain useful insights for better answering to tourists' needs and wants and to exploit user generated images to provide both an economic and a social contribution to the tourism industry

Maximum likelihood estimation based on the Laplace approximation for p2 network regression models

The class of p2 models is suitable for modeling binary relation data in social network analysis. A p2 model is essentially a regression model for bivariate binary responses, featuring within‐dyad dependence and correlated crossed random effects to represent heterogeneity of actors. Despite some desirable properties, these models are used less frequently in empirical applications than other models for network data. A possible reason for this is due to the limited possibilities for this model for accounting for (and explicitly modeling) structural dependence beyond the dyad as can be done in exponential random graph models. Another motive, however, may lie in the computational difficulties existing to estimate such models by means of the methods proposed in the literature, such as joint maximization methods and Bayesian methods. The aim of this article is to investigate maximum likelihood estimation based on the Laplace approximation approach, that can be refined by importance sampling. Practical implementation of such methods can be performed in an efficient manner, and the article provides details on a software implementation using R. Numerical examples and simulation studies illustrate the methodology

Not only the picture to foster tourism: the interplay role of destination area hashtags on Instagram posts

Purpose: Hashtags are important in enriching the content of posts and in obtaining more engagement (Messina, 2007). This study aims at analysing the impact of the combination of the small destination name hashtags with hashtags mentioning the wider destination area by answering at the following research questions: (RQ1) What are the territorial hashtags used in the small destinations’ pictures capture? (RQ2) Do the mentions of the wider area through territorial hashtags impact on small destination post’s engagement? Methods: Based on hashtags’ destination names, a sample of 13,217 posts of 18 Italian small destinations are retrieved (period of higher tourism turnout in 2019). Both content analysis (RQ1) and linear regression models (RQ2) are used. Results: Scholars have never focused on the link between the engagement of a photo and the hashtags related to specific territories. Through this research, we can state that people use hashtags referring to the wider destination area (mainly combining region and nation). The hashtag of a small and niche destination together with the hashtags of the region, neighbouring territories or the nation, can improve the engagement of the related picture in terms of number of likes. Implications: This study confirms the role of the hashtags in enhancing a picture’s engagement contributing to the literature about the consumers’ feedback on online picture by adding the territorial dimension as a variable. From a managerial perspective, it suggests how destination management organizations should use hashtags in Instagram, in order to improve their offerings

The interplay role of destination area hashtags to enhance small destination pictures’ engagement on Instagram

Purpose: Hashtags are important in enriching the content of posts and in obtaining more engagement (Messina, 2007). This study aims at analysing the impact of the combination of the small destination name hashtags with hashtags mentioning the wider destination area by answering at the following research questions: (RQ1) What are the territorial hashtags used in the small destinations’ pictures capture? (RQ2) Do the mentions of the wider area through territorial hashtags impact on small destination post’s engagement? Methods: Based on hashtags’ destination names, a sample of 13,217 posts of 18 Italian small destinations are retrieved (period of higher tourism turnout in 2019). Both content analysis (RQ1) and linear regression models (RQ2) are used. Results: Scholars have never focused on the link between the engagement of a photo and the hashtags related to specific territories. Through this research, we can state that people use hashtags referring to the wider destination area (mainly combining region and nation). The hashtag of a small and niche destination together with the hashtags of the region, neighbouring territories or the nation, can improve the engagement of the related picture in terms of number of likes. Implications: This study confirms the role of the hashtags in enhancing a picture’s engagement contributing to the literature about the consumers’ feedback on online picture by adding the territorial dimension as a variable. From a managerial perspective, it suggests how destination management organizations should use hashtags in Instagram, in order to improve their offerings

Adjusted quasi-profile likelihoods from estimating functions

Higher-order adjustments for a quasi-profile likelihood for a scalar parameter of interest in the presence of nuisance parameters are discussed. Paralleling likelihood asymptotics, these adjustments aim to alleviate some of the problems inherent to the presence of nuisance parameters. Indeed, the estimating equation for the parameter of interest, when the nuisance parameter is substituted with an appropriate estimate, is not unbiased and such a bias can lead to poor inference on the parameter of interest. Following the approach of McCullagh and Tibshirani (1990), here we propose adjustments for the estimating equation for the parameter of interest. Moreover, we discuss two methods for their computation: a bootstrap simulation method, and a first-order asymptotic expression, which can be simplified under an orthogonality assumption. Some examples, in the context of generalized linear models and of robust inference, are provided