Selection of weak VARMA models by modified Akaike's information criteria

This article considers the problem of order selection of the vector autoregressive moving‐average (VARMA) models under the assumption that the errors are uncorrelated but not necessarily independent. These models are called weak VARMA by opposition to the standard VARMA models, also called strong VARMA models, in which the error terms are supposed to be i.i.d. We relax the standard independence assumption to extend the range of application of the VARMA models, allowing us to treat linear representations of general nonlinear processes. We propose a modified version of the Akaike information criterion for identifying the orders of weak VARMA models.     

FAST LINEAR ESTIMATION METHODS FOR VECTOR AUTOREGRESSIVE MOVING-AVERAGE MODELS

Abstract. Three linear methods for estimating parameter values of vector auto-regressive moving-average (VARMA) models which are in general at least an order of magnitude faster than maximum likelihood estimation are developed in this paper. Simulation results for different model structures with varying numbers of component series and observations suggest that the accuracy of these procedures is in most cases comparable with maximum likelihood estimation. Procedures for estimating parameter standard error are also discussed and used for identification of nonzero elements in the VARMA polynomial structures. These methods can also be used to establish the order of the VARMA structure. We note, however, that the primary purpose of these estimates is to generate initial estimates for the nonzero parameters in order to reduce subsequent computational time of more efficient estimation procedures such as exact maximum likelihood.     

A NOTE ON SQUARE ROOT FILTERING FOR VECTOR AUTOREGRESSIVE MOVING-AVERAGE MODELS

Abstract. A simplified version of the square root Kalman filter is obtained for a vector autoregressive moving-average (VARMA) model. The algorithm is computationally more efficient that the standard square root algorithm and its output can be used to compute the likelihood of a VARMA model accurately.     

ALGORITHMS FOR ESTIMATION OF POSSIBLY NONSTATIONARY VECTOR TIME SERIES

Abstract. This paper presents efficient algorithms for evaluating the likelihood function and its gradient of possibly nonstationary vector autoregressive moving-average (VARMA) processes.     

An Extended Portmanteau Test for VARMA Models With Mixing Nonlinear Constraints

Abstract. The portmanteau test is a widely used diagnostic tool for univariate and multivariate time‐series models. Its asymptotic distribution is known for the unconstrained vector autoregressive moving‐average (VARMA) case and for VAR models with constraints on the autoregressive coefficients. In this article, we give conditions under which the test can be applied to constrained VARMA models. Unfortunately, it cannot generally be applied to models with constraints that simultaneously affect the ARMA polynomial coefficients and the covariance matrix of the innovations (mixing constraints). This happens in latent‐variable models such as dynamic factor models (DFM). In addition, when there are constraints on the covariance matrix it seems convenient to check the goodness of fit using the zero‐lag residual covariances. We propose an extended portmanteau test that not only checks the autocorrelations of the residuals but also whether their covariance matrix is consistent with the constraints. We prove that the statistic is asymptotically distributed as a chi‐square for ARMA models under the assumption that the innovations have Gaussian‐like fourth‐order moments. We also show that the test is appropriate for the DFM, Peña–Box model and factor‐structural vector autoregression (FSVAR).     

Singular Points in the Sections of the Concentration Simplexes of n-Component Systems with One Nonvolatile Component: Realization Conditions and Relations

The local properties of vector and scalar fields are analyzed for sections of the diagrams of n-component systems with one nonvolatile component. The properties are shown to differ from the properties of systems in which all components are distributed between the phases. Equations are derived for the algebraic sum of the numbers of singular points for scalar and vector fields in a section of the concentration simplex. Possible diagram structures are considered for three- and four-component systems with one nonvolatile component.     

THE AUTOCORRELATION FUNCTION OF SEASONAL ARMA MODELS

Abstract. This note obtains the theoretical autocorrelation function of an ARMA model with multiplicative seasonality. It is shown that this function can be interpretated as the result of the interaction between the seasonal and regular autocorrelation patterns of the ARMA model. The use of this result makes easier the identification of the structure of the model, is helpful in choosing between a multiplicative or additive seasonal component and leads to a better understanding of the properties of the estimated autocorrelation function of scalar ARMA processes.     

Improved multivariate portmanteau test

A new portmanteau diagnostic test for vector autoregressive moving average (VARMA) models that is based on the determinant of the standardized multivariate residual autocorrelations is derived. The new test statistic may be considered an extension of the univariate portmanteau test statistic suggested by Peňa and Rodríguez (2002) . The asymptotic distribution of the test statistic is derived as well as a chi‐square approximation. However, the Monte–Carlo test is recommended unless the series is very long. Extensive simulation experiments demonstrate the usefulness of this test as well as its improved power performance compared to widely used previous multivariate portmanteau diagnostic check. Two illustrative applications are given.     

Rods glued in engineered hardwood products part I: Experimental results under quasi-static loading

Glued-in Rods (GiR) represent an adhesively bonded structural connection widely used in timber engineering. Up to now, common practice largely focused on softwood. Most structural adhesives have been, accordingly, specifically formulated to perform on softwood, in particular spruce. The increased use of hardwood, and corresponding engineered wood products (EWP), calls for deeper insights regarding GiR for the connection thereof. This paper, the first of a two part series, presents an overview over extensive research carried with 9 adhesives, 3 EWP, and 4 types of rods. Investigations started at component level, by fully characterising all adhesives, EWP, and rods. They were then extended to characterise the behaviour of interfaces, providing by this a methodology for selecting adhesives. Investigations at full scale followed, involving 5 different adhesives, 3 EWP, and 4 rod types. A total of 180 individual samples were tested. The results allowed to draw conclusions about the relationship between performance of GiR connections, and mechanical properties of their components. This relationship, however, has been found to be relatively weak. The companion paper will present a design methodology based on the material properties determined herein, and explain the ambiguous relationship between performance of the GiR and the mechanical properties of the adhesive, wood, and rods     

WEDM Parameter Optimization for Silicon@r-GO/Magneisum Composite Using Taguchi Based GRA Coupled PCA

Silicon - A combination of Taguchi methodology and Grey Relational Analysis (GRA) inturn coupled with principal component analysis (PCA) has been proposed through this paper. This methodology is...     

Functional unfold principal component regression methodology for analysis of industrial batch process data

This work proposes a methodology utilizing functional unfold principal component regression (FUPCR), for application to industrial batch process data as a process modeling and optimization tool. The methodology is applied to an industrial fermentation dataset, containing 30 batches of a production process operating at Novozymes A/S. Following the FUPCR methodology, the final product concentration could be predicted with an average prediction error of 7.4%. Multiple iterations of preprocessing were applied by implementing the methodology to identify the best data handling methods for the model. It is shown that application of functional data analysis and the choice of variance scaling method have the greatest impact on the prediction accuracy. Considering the vast amount of batch process data continuously generated in industry, this methodology can potentially contribute as a tool to identify desirable process operating conditions from complex industrial datasets. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1986–1994, 2016     

ON A SYSTEMATIC DESIGN PROCEDURE FOR SINGLE COMPONENT WATER UTILIZATION SYSTEMS IN PROCESS PLANTS

This paper presents a robust methodology to obtain an optimal design of the single component water/wastewater allocation problem in process plants. The method uses a concentration grid water allocation procedure to obtain preliminary optimal structures. A merging procedure provides the final structures. The use of different water allocation strategies shows that the problem has several alternative solutions.     

Electrochemical C7-Indole Alkenylation via Rhodium Catalysis

Indole derivatives are fundamental structural units in many bioactive compounds and molecular materials. The site-selective C7-functionalization of these moieties has been proven to be extremely challenging due to the inherent reactivity of the C2- and C3-positions. Herein, we report the first electro-C7-alkenylation of indoles. This novel and sustainable methodology provides highly exclusive access to the C7-position devoid of often toxic and expensive chemical oxidants. Moreover, an array of substrates was successfully alkenylated at the C7-position, and versatile product diversification was achieved.     

Development of environmental standards for combined desalination/power generating stations in the Arabian Gulf region

Environmental effects associated with desalination/power generation are classified into intake and discharge components. The intake component consists of entrainment and impingement problems while the discharge component comprises chemical and physical problems associated with the receiving waterbody. Each component is discussed relative to marine communities in the receiving waterbody and a methodology is presented for establishing an environmental managenent policy which will protect these communities.     

On limiting spectral distribution of large sample covariance matrices by VARMA(p,q)

We studied the limiting spectral distribution of large‐dimensional sample covariance matrices of a stationary and invertible VARMA(p,q) model. Relationship of the power spectral density and limiting spectral distribution of large population dimensional covariance matrices of ARMA(p,q) is established. The equation about Stieltjes transform of large‐dimensional sample covariance matrices is also derived. As applications, the classical M‐P law, VAR(1) and VMA(1) can be regarded as special examples.     

Rhodium(III)‐Catalyzed Regioselective Direct C‐2 Alkenylation of Indoles Assisted by the Removable N‐(2‐Pyrimidyl) Group

The C‐2‐alkenylindole unit is a key component of numerous natural products and pharmacophores. However, the intermolecular direct construction of the core structural motif remains challenging in organic synthesis. Here we report a new, efficient, and versatile methodology for the synthesis of C‐2‐alkenylindoles through rhodium(III)‐catalyzed direct C H functionalization of indoles with acrylates under air by employing a metal‐directing group strategy. This strategy gives a rare selectivity for the alkenylation N‐(2‐pyrimidyl)indoles at the C‐2 position and provides the functionalized C‐2‐ alkenylindoles under mild conditions with broad substrate tolerance. An expansion of the methodology has also been demonstrated to, for example, the direct alkenylation of pyrrole and facile deprotection of the pyrimidyl group. All the results suggest that this methodology could be served as a highly attractive alternative for the direct construction of biologically important C‐2‐alkenylindoles.

     

A Novel Assembly of Substituted Pyrroles by Acid‐Catalyzed Sequential Three‐Component Reaction of Amines,Alkynoates, and 1,2‐Diaza‐1,3‐dienes

A novel protocol for the assembly of polysubstituted pyrroles has been developed through the acid‐catalyzed, sequential three‐component reaction of primary aliphatic amines, alkynoates and 1,2‐diaza‐1,3‐dienes (DDs). This methodology proceeds with complete chemo‐/regioselectivity involving first formation of an enamino ester intermediate, in situ Michael addition with azo‐ene compounds and subsequent intramolecular ring closure.     

Heterogeneous Four-Component Extractive-Distillation Systems with One Nonvolatile Component

Heterogeneous four-component systems with one nonvolatile component are considered, and it is demonstrated that the Gibbs–Konovalov law, according to which the singular points of the liquid–vapor tie-line vector field must coincide with those of the equilibrium temperature scalar field, is violated in the interior and at the boundaries of various sections of the concentration tetrahedron.     

Simultaneous design and operation optimization of renewable combined cooling heating and power systems

Combined cooling, heating, and power (CCHP) systems are promising solutions for conserving energy and reducing emissions. This article proposes a new mixed-integer linear programming (MILP) model for simultaneous design and operation optimization of a renewable CCHP system, considering component nonlinear operating characteristics and performance degradation with time. A bi-objective MILP problem is solved to achieve a trade-off between total annual cost (TAC) and greenhouse gas emissions (GHGe). A case study of a commercial region is employed to demonstrate our proposed methodology. The results shows, in comparison with conventional cost minimization, our solution features a tardy increase of 12.8% in TAC and a sharp reduction of 75.5% in GHGe. Moreover, we find that ignoring performance degradation leads to an over-estimation of 2.3–13.7% in system economic performance. The proposed methodology provides an effective and flexible framework for optimal design and operational analysis of renewable CCHP systems.