Soil-Borne Pathogens Associated to New Crops of Cherry Tomato in the Province of Granada Spain

Soil-Borne Pathogens Associated to New Crops of Cherry Tomato in the Province of Granada Spain

Optimization of water procurement decisions in an irrigation district: the role of option contracts

Water supply instability is one of the main risks faced by irrigation districts and farmers. Water procurement decision optimisation is essential in order to increase supply reliability and reduce costs. Water markets, such as spot purchases or water supply option contracts, can make this decision process more flexible. We analyse the potential interest in an option contract for an irrigation district that has access to several water sources. We apply a stochastic recursive mathematical programming model to simulate the water procurement decisions of an irrigation district?s board operating in a context of water supply uncertainty in south-eastern Spain. We analyse what role different option contracts could play in securing its water supply. Results suggest that the irrigation district would be willing to accept the proposed option contract in most cases subject to realistic values of the option contract financial terms. Of nine different water sources, desalination and the option contract are the main substitutes, where the use of either depends on the contract parameters. The contract premium and optioned volume are the variables that have a greater impact on the irrigation district?s decisions. Key words: Segura Basin, stochastic recursive programming, water markets, water supply option contract, water supply risk.

Pratique moderne de la chirurgie /

Mode of access: Internet.

The development of a computer aided system for adaptive budgeting in a confectionery manufacturing firm

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Direct design of high channel-count fiber Bragg grating filters with low index modulation

A novel method for designing high channel-count fiber Bragg gratings (FBGs) is proposed. For the first time, tailored group delay is introduced into the target reflection spectra to obtain a more even distribution of the refractive index modulation. This approach results in the reduction of the maximum refractive index modulation to physically realizable levels. The maximum index modulation reduction factors are all greater than 5.5. This is a significant improvement compared with previously reported results. Numerical results show that the thus designed high channel-count FBG filters exhibit superior characteristics including 30 dB channel isolation, a flat-top and near 100% reflectivity in each channel. © 2012 Optical Society of America.

Performing "Up The Shore": a stylistic analysis of Newfoundland Irish English vowels in the Ferryland dinner theatre

This study is a variationist sociolinguistic analysis of two speech styles, performance and interview, of a dinner theatre troupe in Ferryland on the Southern Shore of Newfoundland. Five actors and ten of their characters are analyzed to test if their vowels change across styles. The study adopts a variationist framework with a Community of Practice model, drawing on Bell’s audience and referee design to argue that the performers’ stage conventions and identity construction are influenced by a third person referee: the Idealized Authentic Newfoundlander (IAN). Under this view the goal of the performer is to both communicate with and entertain the audience, which requires different tactics when speaking. These tactics manifest phonetically and are discussed in a quantitative, statistical analysis of the acoustic measurements of the vowel tokens [variables FACE, KIT, LOT/PALM and GOAT lexical sets with Newfoundland Irish English (NIE) variants] and a qualitative discussion.

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 importância das redes de relacionamento para se formar uma parceria : um estudo de caso na ALESAT

This paper consists in a study case at the Alesat company, which aims at analyzing how the networks from the former companies Ale and Sat influence the formation of partnerships. The study was based on literature by Burt (1992), Granovetter (1973), Uzzi (1997), Contractor and Lorange (1988), Gulati (1998), Child and Faulkner (1998), and others, to verify how important the social relations were between the companies to the formation of a strategic alliance. The research method we adopted analyzed the first partnership between Ale and Sat and the last one that ended up with the merger of the companies resulting in a new company, Alesat. Semi-structured interviews were conducted in 2008 with the council board of the company. Secondary data were also collected from specific web sites from the area, such as ANP, Sindicom and Fecombustíveis, as well as from important newspapers in the market. The primary data were analyzed through the content analysis technique from Triviños (1987). The secondary data were analyzed through the documental analysis technique from Richardson (1985). This way, through the data collected, it can be concluded that the social ties between the companies were important in the partnership, and among the reasons that made the companies get together, the key one was the fact that the union would make possible to the companies act in regions in which they didn t have too much market share, making them a bigger player nationally wise

Friederike Heinzel (Hrsg.): Generationenvermittlung in der Grundschule, Ende der Kindgemäßheit? Bad Heilbrunn: Klinkhardt 2011 [Rezension]

Rezension von: Friederike Heinzel (Hrsg.): Generationenvermittlung in der Grundschule, Ende der Kindgemäßheit? Bad Heilbrunn: Klinkhardt 2011 (240 S.; ISBN 978-3-7815-1814-8)