A Time-Domain Semi-parametric Estimate for Strongly Dependent Continuous-Time Stationary Processes

Abstract.  A covariance-based estimator of the memory parameter of strongly dependent continuous-time stationary processes is proposed. The consistency and asymptotic normality of the estimator are established. All assumptions, the form of the estimator, and the proofs are made in time-domain only.     

Testing for Change‐Points in Long‐Range Dependent Time Series by Means of a Self‐Normalized Wilcoxon Test

We propose a testing procedure based on the Wilcoxon two‐sample test statistic in order to test for change‐points in the mean of long‐range dependent data. We show that the corresponding self‐normalized test statistic converges in distribution to a non‐degenerate limit under the hypothesis that no change occurred and that it diverges to infinity under the alternative of a change‐point with constant height. Furthermore, we derive the asymptotic distribution of the self‐normalized Wilcoxon test statistic under local alternatives, that is, under the assumption that the height of the level shift decreases as the sample size increases. Regarding the finite sample performance, simulation results confirm that the self‐normalized Wilcoxon test yields a consistent discrimination between hypothesis and alternative and that its empirical size is already close to the significance level for moderate sample sizes.     

Efficient Estimation of Seasonal Long‐Range‐Dependent Processes

Abstract. This paper studies asymptotic properties of the exact maximum likelihood estimates (MLE) for a general class of Gaussian seasonal long‐range‐dependent processes. This class includes the commonly used Gegenbauer and seasonal autoregressive fractionally integrated moving average processes. By means of an approximation of the spectral density, the exact MLE of this class are shown to be consistent, asymptotically normal and efficient. Finite sample performance of these estimates is examined by Monte Carlo simulations and it is shown that the estimates behave very well even for moderate sample sizes. The estimation methodology is illustrated by a real‐life Internet traffic example.     

Square‐Root LASSO for High‐Dimensional Sparse Linear Systems with Weakly Dependent Errors

We study the square‐root LASSO method for high‐dimensional sparse linear models with weakly dependent errors. The asymptotic and non‐asymptotic bounds for the estimation errors are derived. Our results cover a wide range of weakly dependent errors, including α‐mixing, ρ‐mixing, ?‐mixing, and m‐dependent types. Numerical simulations are conducted to show the consistency property of square‐root LASSO. An empirical application to financial data highlights the importance of the results and method.     

A Goodness‐of‐Fit Test for Integer‐Valued Autoregressive Processes

For autoregressive count data time series, a goodness‐of‐fit test based on the empirical joint probability generating function is considered. The underlying process is contained in a general class of Markovian models satisfying a drift condition. Asymptotic theory for the test statistic is provided, including a functional central limit theorem for the non‐parametric estimation of the stationary distribution and a parametric bootstrap method. Connections between the new approach and existing tests for count data time series based on moment estimators appear in limiting scenarios. Finally, the test is applied to a real data set.     

A Nonparametric Test for Weak Dependence Against Strong Cycles and its Bootstrap Analogue

Abstract. We examine a test for the hypothesis of weak dependence against strong cyclical components. We show that the limiting distribution of the test is a Gumbel distribution, denoted G(·). However, since G(·) may be a poor approximation to the finite sample distribution, being the rate of the convergence logarithmic [see Hall Journal of Applied Probability (1979) , Vol. 16, pp. 433–439], inferences based on G(·) may not be very reliable for moderate sample sizes. On the other hand, in a related context, Hall [Probability Theory and Related Fields (1991) , Vol. 89, pp. 447–455] showed that the level of accuracy of the bootstrap is significantly better. For that reason, we describe an approach to bootstrapping the test based on Efron's [Annals of Statistics (1979) , Vol. 7, pp. 1–26] resampling scheme of the data. We show that the bootstrap principle is consistent under very mild regularity conditions.     

Block Bootstrap for the Empirical Process of Long‐Range Dependent Data

We consider the bootstrapped empirical process of long‐range dependent data. It is shown that this process converges to a semi‐degenerate limit, where the random part of this limit is always Gaussian. Thus the bootstrap might fail when the original empirical process accomplishes a noncentral limit theorem. However, even in this case our results can be used to estimate a nuisance parameter that appears in the limit of many nonparametric tests under long memory. Moreover, we develop a new resampling procedure for goodness‐of‐fit tests and a test for monotonicity of transformations.     

Subsampling inference for the mean of heavy‐tailed long‐memory time series

In this article, we revisit a time series model introduced by MCElroy and Politis (2007a) and generalize it in several ways to encompass a wider class of stationary, nonlinear, heavy‐tailed time series with long memory. The joint asymptotic distribution for the sample mean and sample variance under the extended model is derived; the associated convergence rates are found to depend crucially on the tail thickness and long memory parameter. A self‐normalized sample mean that concurrently captures the tail and memory behaviour, is defined. Its asymptotic distribution is approximated by subsampling without the knowledge of tail or/and memory parameters; a result of independent interest regarding subsampling consistency for certain long‐range dependent processes is provided. The subsampling‐based confidence intervals for the process mean are shown to have good empirical coverage rates in a simulation study. The influence of block size on the coverage and the performance of a data‐driven rule for block size selection are assessed. The methodology is further applied to the series of packet‐counts from ethernet traffic traces.     

Production Rate‐Dependent Key Performance Indicators for a Systematic Design of Biochemical Downstream Processes

Key performance indicators have become a common tool in bioprocess development, as they allow an objective and independent rating and ranking of process alternatives screened. A disadvantage of all key performance indicators developed so far is their strong focus on costs, which makes them neglect the influence of the production rate on the profit of the process. But, since both production costs and production rate have an equally strong impact, both should be considered when decisions have to be made. Therefore, two new key performance indicators are introduced in this work, one for batch and one for continuous processing. They combine product yield, purity performance, variable manufacturing costs, and production rate to one objective. Thereby, misguided trade‐offs between these four individual measures can be replaced by one key performance indicator. This ensures a systematic development of biochemical downstream processes.     

Unique UV absorbance for triphenylimidazole‐based polymer

In this study, a novel vinyl polymer containing 2,4,5‐triphenylimidazole side groups was synthesized through conventional radical polymerization, which possesses the characteristic of temperature‐dependent absorbance in the near‐ultraviolet (UV) spectral region. The linear dependence of UV absorbance on temperature was confirmed for the polymer both in solid state and in solution by experiment and also supported by the results of theoretical investigation. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Ligand‐Biased and Probe‐Dependent Modulation of Chemokine Receptor CXCR3 Signaling by Negative Allosteric Modulators

Over the last decade, functional selectivity (or ligand bias) has evolved from being a peculiar phenomenon to being recognized as an essential feature of synthetic ligands that target G protein‐coupled receptors (GPCRs). The CXC chemokine receptor 3 (CXCR3) is an outstanding platform to study various aspects of biased signaling, because nature itself uses functional selectivity to manipulate receptor signaling. At the same time, CXCR3 is an attractive therapeutic target in the treatment of autoimmune diseases and cancer. Herein we report the discovery of an 8‐azaquinazolinone derivative (N‐{1‐[3‐(4‐ethoxyphenyl)‐4‐oxo‐3,4‐dihydropyrido[2,3‐d]pyrimidin‐2‐yl]ethyl}‐4‐(4‐fluorobutoxy)‐N‐[(1‐methylpiperidin‐4‐yl)methyl]butanamide, 1 b ) that can inhibit CXC chemokine 11 (CXCL11)‐dependent G protein activation over β‐arrestin recruitment with 187‐fold selectivity. This compound also demonstrates probe‐dependent activity, that is, it inhibits CXCL11‐ over CXCL10‐mediated G protein activation with 12‐fold selectivity. Together with a previously reported biased negative allosteric modulator from our group, the present study provides additional information on the molecular requirements for allosteric modulation of CXCR3.     

A systematic comparison of PCA‐based Statistical Process Monitoring methods for high‐dimensional,time‐dependent Processes

High‐dimensional and time‐dependent data pose significant challenges to Statistical Process Monitoring. Most of the high‐dimensional methodologies to cope with these challenges rely on some form of Principal Component Analysis (PCA) model, usually classified as nonadaptive and adaptive. Nonadaptive methods include the static PCA approach and Dynamic Principal Component Analysis (DPCA) for data with autocorrelation. Methods, such as DPCA with Decorrelated Residuals, extend DPCA to further reduce the effects of autocorrelation and cross‐correlation on the monitoring statistics. Recursive Principal Component Analysis and Moving Window Principal Component Analysis, developed for nonstationary data, are adaptive. These fundamental methods will be systematically compared on high‐dimensional, time‐dependent processes (including the Tennessee Eastman benchmark process) to provide practitioners with guidelines for appropriate monitoring strategies and a sense of how they can be expected to perform. The selection of parameter values for the different methods is also discussed. Finally, the relevant challenges of modeling time‐dependent data are discussed, and areas of possible further research are highlighted. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1478–1493, 2016     

A Discussion of the Kamlet‐Jacobs Formula for the Detonation Pressure

The main features of the Kamlet‐Jacobs formula for the detonation pressure of C H N O explosives are analytically derived from a BKW (Becker‐Kistiakowsky‐Wilson) equation of state of the detonation products. In the derivation, well‐known typical values at the Chapman‐Jouguet state, in particular the nearly constant value of the relative volume of the detonation products, are used.     

Volume‐Dependent Self‐Ignition Temperatures for Explosive Materials

In this paper the differential equation of thermal explosion in the steady‐state approximation is established and solved. This solution is based on Fourier’s heat equation along with the Arrhenius heat source. The theory allows to draw conclusions on the thermal behaviour of any substance. The critical ambient temperature appears as a function of the apparent activation energy, Arrhenius temperature rate, temperature diffusivity, and the volume. The thermal stability of a chemically reactive material may be determined by means of these dependencies. Different volumes of five explosive materials were exposed to hot storage tests. These experiments generate appropriate kinetic parameters for these materials that are compared with known values from literature. Once these parameters are known, self‐ignition temperatures can be calculated for any arbitrary volume and boundary conditions. This is of major importance in the safe transportation and storage of explosives, munitions, and weapons.     

A Dependence Metric for Possibly Nonlinear Processes

Abstract.  A transformed metric entropy measure of dependence is studied which satisfies many desirable properties, including being a proper measure of distance . It is capable of good performance in identifying dependence even in possibly nonlinear time series, and is applicable for both continuous and discrete variables. A nonparametric kernel density implementation is considered here for many stylized models including linear and nonlinear MA, AR, GARCH, integrated series and chaotic dynamics. A related permutation test of independence is proposed and compared with several alternatives.     

A Macro‐Damaged Viscoelastoplastic Model for Thermomechanical and Rate‐Dependent Behavior of Glassy Polymers

The primary objective of this paper is to develop a macro‐damaged viscoelastoplastic constitutive model to describe large deformation mechanical behavior for glassy polymers at various kinds of experimental conditions. First of all, quasi‐static and dynamic tension and compression tests were carried out to obtain stress–strain responses over wide range of rates and temperatures for two glassy polymeric materials, polymethylmethacrylate and polycarbonate. Then a phenomenological macroscopic damage model, covering effects of strain rates, temperatures, and effective strain, is incorporated into the constitutive model in the previous work. Furthermore, two distinct criteria are applied to predict the damage evolution affected by strain rate and temperature at elastic phase and plastic phase, respectively. The validations of the novel developed damaged model are made by the better matches with the testing data compared with the original undamaged model, and excellent agreements with the experimental result in all cases.

     

A Family of Markov‐Switching Garch Processes

This paper develops a family of Markov‐switching GARCH (MSG) processes that not only encompasses some specifications in the literature, but also can be regarded as a Markov‐switching version of the family of GARCH processes introduced by He and Teräsvirta (J. Econometrics, 1999 , 92, 173–192). Some structural properties of this new family of MSG processes are considered. First, a sufficient and necessary condition for the existence of the strictly stationary solution of the family of MSG processes is presented. Moreover, we also give a sufficient and necessary condition for the existence of the strictly stationary solution of the family of MSG processes with finite δ‐order moment. In particular, a definition of so‐called family of integrated MSG processes is introduced and its stationarity is also discussed. Next, the general conditions for the existence of any order moment of the family of MSG processes are derived. Finally, by means of a new renewal theorem, we describe the tail of the marginal distribution of the family of MSG processes.