Earnings Management and Investor Protection: An International Comparison

Dependence modeling with copulas

Cultural Tourism: a review of recent research and trends

Over the past decades, detailed individual building energy models (BEM) on the one side and regional and country-level building stock models on the other side have become established modes of analysis for building designers and energy policy makers, respectively. More recently, these two toolsets have begun to merge into hybrid methods that are meant to analyze the energy performance of neighborhoods, i.e. several dozens to thousands of buildings. This paper reviews emerging simulation methods and implementation workflows for such bottom-up urban building energy models (UBEM). Simulation input organization, thermal model generation and execution, as well as result validation, are discussed successively and an outlook for future developments is presented.

Urban building energy modeling – A review of a nascent field

The notion of defining a cluster as a component in a mixture model was put forth by Tiedeman in 1955; since then, the use of mixture models for clustering has grown into an important subfield of classification. Considering the volume of work within this field over the past decade, which seems equal to all of that which went before, a review of work to date is timely. First, the definition of a cluster is discussed and some historical context for model-based clustering is provided. Then, starting with Gaussian mixtures, the evolution of model-based clustering is traced, from the famous paper by Wolfe in 1965 to work that is currently available only in preprint form. This review ends with a look ahead to the next decade or so.

/pdf/model-based-clustering-14s7ywfrft.pdf

Model-Based Clustering

Due to their large worldwide adoption, Social Network Sites (SNSs) have been widely used in many global events as an important source to spread news and information. While the searchability and persistence of this information make it ideal for sociological research, a quantitative approach is still challenging because of the size and complexity of the data. In this paper we provide a first analysis of Friendfeed, a well-known and feature-rich SNS.

Social network data and practices: the case of friendfeed

The main aim of this work is to study the relationship between cultural tourism and temporary art exhibitions by means of a panel data analysis. In literature, the capability of cultural attractions, e.g. art exhibitions, of increasing the tourist flow has not been investigated yet and we start to fill this gap. Our data set consists of monthly observed variables on 52 Italian provinces over the period 2003–2007. Our response variable is the amount of hotel arrivals and the set of covariates includes the amount of visitors of temporary exhibitions of ancient, modern and contemporary art plus a set of variables that contribute to tourist arrivals, like the kind of destination, the attraction capability of each province and so on. Of particular interest is to discover the art sector that has the strongest influence on the tourist flow towards Italy by keeping into account both the temporal dynamic and the heterogeneity of the Italian provinces. To this aim, we discuss the static and dynamic error component regression models from both the theoretical and practical point of view. The estimated two-way fixed effects regression models suggest that all the three sectors contribute to the tourist flow but in a quite different fashion. On the basis of our analysis we derive some indications for the policy maker in the field of cultural tourism.

Cultural tourism and temporary art exhibitions in Italy: a panel data analysis

Supplier Total Cost of Ownership (TCO) is a widely-known approach for determining the overall cost generated by a supplier relationship, but its adoption is still limited. The complex calculations involved – and in particular the activity-based costing procedure for computing the cost of managing the relationship – pose a major obstacle to widespread TCO implementation. The purpose of this work is to formulate a Data Envelopment Analysis application (denoted ‘TCO-based DEA’) that can act as a proxy for TCO, and to test its ability to approximate the results of TCO with less effort. The study is based on the analysis of two categories of suppliers (74 in total) of a medium-sized Italian mechanical engineering company. The results show that TCO-based DEA is able to significantly approximate the outcomes of TCO, for both the efficiency indexes and rankings of suppliers, whilst requiring substantially less effort to perform the analysis. To our knowledge, this is the first study to develop a DEA-based tool for approximating TCO and to test it in a real-world setting. The research shows significant potential within the supply chain management field. In particular, TCO-based DEA can be used for analysing suppliers’ performance, rationalising and reducing the supplier base, assisting the negotiation process.

/pdf/supplier-s-total-cost-of-ownership-evaluation-a-data-3fzp1fbuae.pdf

Supplier's total cost of ownership evaluation: a data envelopment analysis approach

In this work we introduce a copula-based method for imputing missing data by using conditional density functions of the missing variables given the observed ones. In theory, such functions can be derived from the multivariate distribution of the variables of interest. In practice, it is very difficult to model joint distributions and derive conditional distributions, especially when the margins are different. We propose a natural solution to the problem by exploiting copulas so that we derive conditional density functions through the corresponding conditional copulas. The approach is appealing since copula functions enable us (1) to fit any combination of marginal distribution functions, (2) to take into account complex multivariate dependence relationships and (3) to model the marginal distributions and the dependence structure separately. We describe the method and perform a Monte Carlo study in order to compare it with two well-known imputation techniques: the nearest neighbour donor imputation and the regression imputation by EM algorithm. Our results indicate that the proposal compares favourably with classical methods in terms of preservation of microdata, margins and dependence structure.

Exploring copulas for the imputation of complex dependent data

The main aim of this work is the study of clustering dependent data by means of copula functions. Copulas are popular multivariate tools whose importance within clustering methods has not been investigated yet in detail. We propose a new algorithm (CoClust in brief) that allows to cluster dependent data according to the multivariate structure of the generating process without any assumption on the margins. Moreover, the approach does not require either to choose a starting classification or to set a priori the number of clusters; in fact, the CoClust selects them by using a criterion based on the log–likelihood of a copula fit. We test our proposal on simulated data for different dependence scenarios and compare it with a model–based clustering technique. Finally, we show applications of the CoClust to real microarray data of breast-cancer patients.

F. Marta L. Di Lascio

Papers

Social network data and practices: the case of friendfeed

Cultural tourism and temporary art exhibitions in Italy: a panel data analysis

Supplier's total cost of ownership evaluation: a data envelopment analysis approach

Exploring copulas for the imputation of complex dependent data

A Copula-Based Algorithm for Discovering Patterns of Dependent Observations