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We present a novel approach to the portfolio selection problem for a multiperiod investor facing multiple risky assets, trading constraints, and return predictability. Our objective is to maximize mean-variance utility while addressing the computational challenges arising from the curse of dimensionality associated with dynamic programming in the presence of trading constraints. To overcome this, we employ model predictive control, a computationally efficient method for solving the problem. Additionally, we propose the use of a non-parametric Bayesian model, specifically the hierarchical Dirichlet process based Hidden Markov Model (HDP-HMM), to predict the multiperiod mean and covariance of returns. Then, we consider a time-varying maximum drawdown to adjust the risk aversion, which can effectively cope with the limit loss problems. Through extensive simulation studies and empirical analysis, we demonstrate that trading strategies based on our proposed method outperform existing approaches in out-of-sample performance. [ABSTRACT FROM AUTHOR]

There is an increasing amount of literature focused on Bayesian computational methods to address problems with intractable likelihood. One approach is a set of algorithms known as Approximate Bayesian Computational (ABC) methods. One of the problems with these algorithms is that their performance depends on the appropriate choice of summary statistics, distance measure and tolerance level. To circumvent this problem, an alternative method based on the empirical likelihood has been introduced. This method can be easily implemented when a set of constraints, related to the moments of the distribution, is specified. However, the choice of the constraints is sometimes challenging. To overcome this difficulty, we propose an alternative method based on a bootstrap likelihood approach. The method is easy to implement and in some cases is actually faster than the other approaches considered. We illustrate the performance of our algorithm with examples from population genetics, time series and stochastic differential equations. We also test the method on a real dataset. [ABSTRACT FROM AUTHOR]

In the big data era there is a growing need to model the main features of large and non-trivial data sets. This paper proposes a Bayesian nonparametric prior for modelling situations where data are divided into different units with different densities, allowing information pooling across the groups. Leisen and Lijoi [(2011), ‘Vectors of Poisson–Dirichlet processes’,J. Multivariate Anal., 102, 482–495] introduced a bivariate vector of random probability measures with Poisson–Dirichlet marginals where the dependence is induced through a Lévy's Copula. In this paper the same approach is used for generalising such a vector to the multivariate setting. A first important contribution is the derivation of the Laplace functional transform which is non-trivial in the multivariate setting. The Laplace transform is the basis to derive the exchangeable partition probability function (EPPF) and, as a second contribution, we provide an expression of the EPPF for the multivariate setting. Finally, a novel Markov Chain Monte Carlo algorithm for evaluating the EPPF is introduced and tested. In particular, numerical illustrations of the clustering behaviour of the new prior are provided. [ABSTRACT FROM AUTHOR]

[Display omitted] • Pre-allocation of raw gases by means of split-mixing operation and multi-feeding. • Analysis for the effect of parameters on multiple gas feeds absorption. • Mass transfer of H 2 S strengthened through optimizing column profiles. • Simultaneous energy saving and purity improvement compared with conventional process. Sweetening process is essential in removing H 2 S and CO 2 from refinery gases for environmental and safety concerns. In industrial production, owing to the investment cost, multiple sour gases with various compositions are premixed and sent to a single sweetening plant, leading to unnecessary energy consumption caused by ignoring characteristics of each sour gas. Therefore, the multiple gas feeds absorption was analyzed through orthogonal test method and sensitivity analysis. Moreover, a pre-allocation of raw gases along with multi-feeding operation was proposed to optimize the temperature and concentration distribution in the absorber, so that the mass transfer was significantly strengthened. Based on this, a sweetening process with multiple gas feeds was developed to realize the simultaneous optimization of mass and energy. The rigorous simulation was carried out and the model validity had been verified with experimental and industrial data. Finally, a refinery in Shandong, China was taken as an example. With the proposed process, the purity of H 2 S in acid gas was increased by 2.55%, while the energy consumption and the overall exergy loss were reduced by 6.5% and 18.75%, respectively. [ABSTRACT FROM AUTHOR]

We propose a novel method for regression adjustment in approximate Bayesian computation to help improve the accuracy and computational efficiency of the posterior inference. The proposed method uses random forest regression to model the connection between summary statistics and the parameters of interest. Compared with existing approaches, the proposed method bypasses the need of preselection of summary statistics in the model, and is capable of capturing the potential nonlinear relationship between the parameters of interest and summary statistics. We also introduce a measure to quantify the importance of each summary statistic used in the model. We study the asymptotic properties of the proposed estimator and show that it has an excellent finite-sample numerical performance via two simulation examples and an application to a population genetic study. [ABSTRACT FROM AUTHOR]

• State-Task Network representation for distillation sequence with sidestream column • Nonsharp distillation design through modified Fenske-Underwood-Gilliland method • Four-component case with mass interaction between reaction and separation systems • 14.11% and 5.70% reduction in operational cost and TAC for reference case In this work, a comprehensive design method for three-section sidestream columns along with a novel State Task Network representation including both conventional and sidestream distillation sequence configurations is present. The distillation network is embedded in the superstructure-based framework for reaction-separation-recycle process synthesis to explore the mass interaction in different sub-systems. In the superstructure, complete interconnections among reactor modules as well as necessary connections between reactor modules and distillation columns are involved to guarantee opportune coupled degree. Based on this, component distribution in distillation through Underwood equations is introduced to provide desired products with required purity and multiple component mixtures as feedback to the reactor network, achieving the optimization of mass interaction in the whole flowsheet. Furthermore, Fenske-Underwood-Gilliland shortcut method to design columns with and without sidestream, and finite element with orthogonal collocation approach to design PFRs are employed, so that reasonable tradeoff between model accuracy and solution difficulty is achieved. Finally, two literature examples are performed to demonstrate the feasibility and validity of the proposed methodology. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel Bayesian data augmentation approach for estimating the parameters of the generalized logistic regression model. We propose a Pólya–Gamma sampler algorithm that allows us to sample from the exact posterior distribution, rather than relying on approximations. A simulation study illustrates the flexibility and accuracy of the proposed approach to capture heavy and light tails in binary response data of different dimensions. The algorithm performance is tested on simulated data. Furthermore, the methodology is applied to two different real datasets, where we demonstrate that the Pólya–Gamma sampler provides more precise estimates than the empirical likelihood method, outperforming approximate approaches. [ABSTRACT FROM AUTHOR]

The number of immigrants moving to and settling in Europe has increased over the past decade, making migration one of the most topical and pressing issues in European politics. It is without a doubt that immigration has multiple impacts, in terms of economy, society and culture, on the European Union. It is fundamental to policy-makers to correctly evaluate people's attitudes towards immigration when designing integration policies. Of critical interest is to properly discriminate between subjects who are favourable towards immigration from those who are against it. Public opinions on migration are typically coded as binary responses in surveys. However, traditional methods, such as the standard logistic regression, may suffer from computational issues and are often not able to accurately model survey information. In this paper we propose an efficient Bayesian approach for modelling binary response data based on the generalized logistic regression. We show how the proposed approach provides an increased flexibility compared to traditional methods, due to its ability to capture heavy and light tails. The power of our methodology is tested through simulation studies and is illustrated using European Social Survey data on immigration collected in different European countries in 2016–2017. [ABSTRACT FROM AUTHOR]