In this study, we apply the Fredholm-type integral equation method to derive the explicit formulas of the average run length (ARL) for an autoregressive moving average process with explanatory variables (ARMAX(p,q,r))...
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Several classification methods assume that the underlying distributions follow tree-structured graphical models. Indeed, trees capture statistical dependencies between pairs of variables, which may be crucial to attai...
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Several classification methods assume that the underlying distributions follow tree-structured graphical models. Indeed, trees capture statistical dependencies between pairs of variables, which may be crucial to attaining low classification errors. In this setting, the optimal classifier is linear in the log-transformed univariate and bivariate densities that correspond to the tree edges. In practice, observed data may not be well approximated by trees. Yet, motivated by the importance of pairwise dependencies for accurate classification, here we propose to approximate the optimal decision boundary by a sparse linear combination of the univariate and bivariate log-transformed densities. Our proposed approach is semi-parametric in nature: we non-parametrically estimate the univariate and bivariate densities, remove pairs of variables that are nearly independent using the Hilbert-Schmidt independence criterion, and finally construct a linear SVM using the retained log-transformed densities. We demonstrate on synthetic and real data sets, that our classifier, named SLB (sparse log-bivariate density), is competitive with other popular classification methods.
Cross-validation is the de facto standard for predictive model evaluation and selection. In proper use, it provides an unbiased estimate of a model's predictive performance. However, data sets often undergo variou...
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The primary objective of this paper is to review several results available in the statistical literature pertaining to the problem of hierarchical estimation. Some relevant extensions are derived for the problem of re...
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The primary objective of this paper is to review several results available in the statistical literature pertaining to the problem of hierarchical estimation. Some relevant extensions are derived for the problem of reconstructing the globally optimal estimate based on local estimates transmitted by feeder nodes. Their utilisty is next demonstrated with characterizations and examples pertaining to common scenarios in decentralized estimation problems.
In the past decades, clean and renewable energy has gained increasing attention due to a global effort on carbon footprint reduction. In particular, Saudi Arabia is gradually shifting its energy portfolio from an excl...
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The Brazilian federal government regulates the process for procurement of Information Technology (IT) solutions through specific legislation named Regulatory Instruction - RI N° 04/2010. This process consists of ...
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The Brazilian federal government regulates the process for procurement of Information Technology (IT) solutions through specific legislation named Regulatory Instruction - RI N° 04/2010. This process consists of three phases: procurement planning, supplier selection and contract management. This work helps (i) specify and validate an approach for traceability between legal requirements and documents created in the procurement process of IT solutions; (ii) reduce manual work for the verification of legal compliance in the set of artifacts produced; and (iii) support activities of auditing and inspection during and after the procurement of IT solutions by the Brazilian federal government.
In this paper, we propose a regularized mixture probabilistic model to cluster matrix data and apply it to brain signals. The approach is able to capture the sparsity (low rank, small/zero values) of the original sign...
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We introduce a machine-learning-based framework for constructing continuum a non-Newtonian fluid dynamics model directly from a microscale description. Dumbbell polymer solutions are used as examples to demonstrate th...
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We introduce a machine-learning-based framework for constructing continuum a non-Newtonian fluid dynamics model directly from a microscale description. Dumbbell polymer solutions are used as examples to demonstrate the essential ideas. To faithfully retain molecular fidelity, we establish a micro-macro correspondence via a set of encoders for the microscale polymer configurations and their macroscale counterparts, a set of nonlinear conformation tensors. The dynamics of these conformation tensors can be derived from the microscale model, and the relevant terms can be parametrized using machine learning. The final model, named the deep non-Newtonian model (DeePN2), takes the form of conventional non-Newtonian fluid dynamics models, with a generalized form of the objective tensor derivative that retains the microscale interpretations. Both the formulation of the dynamic equation and the neural network representation rigorously preserve the rotational invariance, which ensures the admissibility of the constructed model. Numerical results demonstrate the accuracy of DeePN2 where models based on empirical closures show limitations.
Population growth, climate change, and the occurrence of droughts have intensified the water supply crises in the Brazilian Semiarid region. The objective of this study was to identify the most relevant measures for t...
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Facing increasing societal and economic pressure, many countries have established strategies to develop renewable energy portfolios, whose penetration in the market can alleviate the dependence on fossil fuels. In the...
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