Robust Bayesian analysis of heavy-tailed stochastic volatility models using scale mixtures of normal distributions

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Identification of stock units of Mullus barbatus (Linnaeus, 1758) in the Italian seas: development of genomic 2b-RAD markers and biological variation analysis.

This thesis is settled within the STOCKMAPPING project, which represents one of the studies that were developed in the framework of RITMARE Flagship project. The main goals of STOCKMAPPING were the creation of a genomic mapping for stocks of demersal target species and the assembling of a database of population genomic, in order to identify stocks and stocks boundaries. The thesis focuses on three main objectives representing the core for the initial assessment of the methodologies and structure that would be applied to the entire STOCKMAPPING project: individuation of an analytical design to identify and locate stocks and stocks boundaries of Mullus barbatus, application of a multidisciplinary approach to validate biological methods and an initial assessment and improvement for the genotyping by sequencing technique utilized (2b-RAD). The first step is the individuation of an analytical design that has to take in to account the biological characteristics of red mullet and being representative for STOCKMAPPING commitments. In this framework a reduction and selection steps was needed due to budget reduction. Sampling areas were ranked according the individuation of four priorities. To guarantee a multidisciplinary approach the biological data associated to the collected samples were used to investigate differences between sampling areas and GSAs. Genomic techniques were applied to red mullet for the first time so an initial assessment of molecular protocols for DNA extraction and 2b-RAD processing were needed. At the end 192 good quality DNAs have been extracted and eight samples have been processed with 2b-RAD. Utilizing the software Stacks for sequences analyses a great number of SNPs markers among the eight samples have been identified. Several tests have been performed changing the main parameter of the Stacks pipeline in order to identify the most explicative and functional sets of parameters.

Stock boundaries of Parapenaeus longirostris (Lucas, 1846) populations in the Italian seas: Development of genomic 2b-RAD markers and biological variation analysis.

This thesis is developed in the contest of Ritmare project WP1, which main objective is the development of a sustainable fishery through the identification of populations boundaries in commercially important species in Italian Seas. Three main objectives are discussed in order to help reach the main purpose of identification of stock boundaries in Parapenaeus longirostris: 1 -Development of a representative sampling design for Italian seas; 2 -Evaluation of 2b-RAD protocol; 3 -Investigation of populations through biological data analysis. First of all we defined and accomplished a sampling design which properly represents all Italian seas. Then we used information and data about nursery areas distribution, abundance of populations and importance of P. longirostris in local fishery, to develop an experimental design that prioritize the most important areas to maximize the results with actual project funds. We introduced for the first time the use of 2b-RAD on this species, a genotyping method based on sequencing the uniform fragments produced by type IIB restriction endonucleases. Thanks to this method we were able to move from genetics to the more complex genomics. In order to proceed with 2b-RAD we performed several tests to identify the best DNA extraction kit and protocol and finally we were able to extract 192 high quality DNA extracts ready to be processed. We tested 2b-RAD with five samples and after high-throughput sequencing of libraries we used the software “Stacks” to analyze the sequences. We obtained positive results identifying a great number of SNP markers among the five samples. To guarantee a multidisciplinary approach we used the biological data associated to the collected samples to investigate differences between geographical samples. Such approach assures continuity with other project, for instance STOCKMED, which utilize a combination of molecular and biological analysis as well.

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^

Wind tunnel analysis of the aerodynamic loads on rolling stock over railway embankments: the effect of shelter windbreaks

Wind-flow pattern over embankments involves an overexposure of the rolling stock travelling on them to wind loads. Windbreaks are a common solution for changing the flow characteristic in order to decrease unwanted effects induced by the presence of crosswind. The shelter effectiveness of a set of windbreaks placed over a railway twin-track embankment is experimentally analysed. A set of two-dimensional wind tunnel tests are undertaken and results corresponding to pressure tap measurements over a section of a typical high-speed train are herein presented.The results indicate that even small-height windbreaks provide sheltering effects to the vehicles. Also, eaves located at the windbreak tips seem to improve their sheltering effect.

New York laws affecting business corporations, containing the Business corporations law, General corporation law, Stock corporation law, applying provisions of the Tax law, including the Stock transfer tax act, and the Uniform stock transfer act and a synoptic analysis. Revised to May 10, 1924.

Mode of access: Internet.

The stock market barometer : a study of its forecast value based on Charles H. Dow's theory of the price movement. With an analysis of the market and its history since 1897 /

Reprint of 1922 ed. with a new introduction.

Mixture modelling for cluster analysis

Cluster analysis via a finite mixture model approach is considered. With this approach to clustering, the data can be partitioned into a specified number of clusters g by first fitting a mixture model with g components. An outright clustering of the data is then obtained by assigning an observation to the component to which it has the highest estimated posterior probability of belonging; that is, the ith cluster consists of those observations assigned to the ith component (i = 1,..., g). The focus is on the use of mixtures of normal components for the cluster analysis of data that can be regarded as being continuous. But attention is also given to the case of mixed data, where the observations consist of both continuous and discrete variables.

Performance analysis using equivariant kernel density estimator in nonlinear mixture

This paper investigates the performance analysis of separation of mutually independent sources in nonlinear models. The nonlinear mapping constituted by an unsupervised linear mixture is followed by an unknown and invertible nonlinear distortion, are found in many signal processing cases. Generally, blind separation of sources from their nonlinear mixtures is rather difficult. We propose using a kernel density estimator incorporated with equivariant gradient analysis to separate the sources with nonlinear distortion. The kernel density estimator parameters of which are iteratively updated to minimize the output independence expressed as a mutual information criterion. The equivariant gradient algorithm has the form of nonlinear decorrelation to perform the convergence analysis. Experiments are proposed to illustrate these results.