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Abstract: We revisit an old Russian paper by V.V. Dynnik, R. Heinrich and E.E. Sel’kov (1980a,b) describing: “A mathematical model of carbohydrate energy metabolism. Interaction between glycolysis, the Krebs cycle and the H-transporting shuttles at varying ATPases load”. We analyse the model mathematically and calculate the control coefficients as a function of ATPase loads. We also evaluate the structure of the metabolic network in terms of elementary flux modes. We show how this model can respond to an ATPase load as well as to the glucose supply. We also show how this simple model can help in understanding the articulation between the major blocks of energetic metabolism, i.e. glycolysis, the Krebs cycle and the H-transporting shuttles. [Copyright &y& Elsevier]

Abstract: In this note, we improve some results of Granville and Soundararajan on the distribution of values of the truncated random Euler product , where the are independent random variables, taking the values ±1 with equal probability and 0 with probability . [Copyright &y& Elsevier]

In a previous paper, the authors introduced a flexible methodology for reconstructing mechanical sources in the frequency domain from prior local information on both their nature and location over a linear and time invariant structure. The proposed approach was derived from Bayesian statistics, because of its ability in mathematically accounting for experimenter’s prior knowledge. However, since only the Maximum a Posteriori estimate was computed, the posterior uncertainty about the regularized solution given the measured vibration field, the mechanical model and the regularization parameter was not assessed. To answer this legitimate question, this paper fully exploits the Bayesian framework to provide, from a Markov Chain Monte Carlo algorithm, credible intervals and other statistical measures (mean, median, mode) for all the parameters of the force reconstruction problem. [ABSTRACT FROM AUTHOR]

Most commonly used distributions on the unit hypersphere S k − 1 = { v ∈ R k : v ⊤ v = 1 } , k ≥ 2 , assume that the data are rotationally symmetric about some direction θ ∈ S k − 1 . However, there is empirical evidence that this assumption often fails to describe reality. We study in this paper a new class of skew-rotationally-symmetric distributions on S k − 1 that enjoy numerous good properties. We discuss the Fisher information structure of the model and derive efficient inferential procedures. In particular, we obtain the first semi-parametric test for rotational symmetry about a known direction. We also propose a second test for rotational symmetry, obtained through the definition of a new measure of skewness on the hypersphere. We investigate the finite-sample behavior of the new tests through a Monte Carlo simulation study. We conclude the paper with a discussion about some intriguing open questions related to our new models. [ABSTRACT FROM AUTHOR]

This paper presents a time-domain method for multi-exciter random environment tests. Traditional random environment test theory has been formulated in the frequency domain, where an important step is taking the inverse of the frequency response function matrices (FRFMs). The accuracy of this inversion tends to be poor, particularly at frequencies near lightly damped resonances. The currently used control algorithms face difficulties in suppressing abnormal spectral lines caused by this inverse problem. In this paper, traditional formulations of the environment test are reformed, and the time-domain method is adopted; this results in a more precise inverse operation in environment tests. To achieve this, reference spectra are converted into time-domain response signals. The finite long driving signals are derived by the state-space method with estimated state vectors. During the process, the inverse of rank-deficient Toeplitz matrices are stabilized with truncated singular value decomposition (TSVD) to suppress all abnormally high-level components in the driving forces; thus, overall, the spectra lines produced by noise within the frequency band are filtered out. A numerical simulation of a single-axis random vibration test of a cantilever beam is conducted using the traditional frequency-domain procedure (FDP) and the proposed time-domain procedure (TDP). The response spectra generated by both procedures are tested by control algorithms, and the result shows that responses generated by the proposed TDP are more easily controlled. The conditions of stability for both the FDP and the TDP are also determined and introduced in the simulation. Moreover, a multi-axis vibration experiment further validates the effectiveness of the TDP. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we address the problem of parameter estimation of a two-dimensional (2D) chirp model under the assumption that the errors are stationary. We define a periodogram-type function, which is based on the extension of the 2D periodogram function, defined for a 2D sinusoidal model, to the 2D chirp model. We put forward an alternative to the least squares estimators (LSEs), called the approximate least squares estimators (ALSEs). The proposed estimators take less time to compute and at the same time, they are asymptotically equivalent to the LSEs. Moreover the asymptotic properties of these estimators are obtained under slightly weaker assumptions than those required for the LSEs. Finally, we propose a sequential method for the estimation of the unknown parameters of a multiple component 2D chirp model. This method significantly reduces the computational difficulty involved in finding the usual LSEs and the ALSEs. To see how the proposed method works, we perform some simulation studies and analyze a data set for illustrative purposes. [ABSTRACT FROM AUTHOR]

Based on the notions of colored and absolute excedances introduced by Bagno and Garber and their affine versions introduced by Mongelli, we compute the signed generating function of such statistics. Moreover, whenever possible, we derive a combinatorial interpretation of the coefficients of such generating functions. This paper is inspired by a paper of S. Sivasubramanian in which the author enumerates signed statistics on the group of classical permutations. [ABSTRACT FROM AUTHOR]

Unequally spaced transducer rings make it possible to extend the range of detectable azimuthal modes. The disadvantage is that the response of the mode detection algorithm to a single mode is distributed over all detectable modes, similarly to the Point Spread Function of Conventional Beamforming with microphone arrays. With multiple modes the response patterns interfere, leading to a relatively high “noise floor” of spurious modes in the detected mode spectrum, in other words, to a low dynamic range. In this paper a deconvolution strategy is proposed for increasing this dynamic range. It starts with separating the measured sound into shaft tones and broadband noise. For broadband noise modes, a standard Non-Negative Least Squares solver appeared to be a perfect deconvolution tool. For shaft tones a Matching Pursuit approach is proposed, taking advantage of the sparsity of dominant modes. The deconvolution methods were applied to mode detection measurements in a fan rig. An increase in dynamic range of typically 10–15 dB was found. [ABSTRACT FROM AUTHOR]

Intuition suggests that for a conditional to be evaluated as true, there must be some kind of connection between its component clauses. In this paper, we formulate and test a new psychological theory to account for this intuition. We combined previous semantic and psychological theorizing to propose that the key to the intuition is a relevance-driven, satisficing-bounded inferential connection between antecedent and consequent. To test our theory, we created a novel experimental paradigm in which participants were presented with a soritical series of objects, notably colored patches (Experiments 1 and 4) and spheres (Experiment 2), or both (Experiment 3), and were asked to evaluate related conditionals embodying non-causal inferential connections (such as “If patch number 5 is blue, then so is patch number 4”). All four experiments displayed a unique response pattern, in which (largely determinate) responses were sensitive to parameters determining inference strength, as well as to consequent position in the series, in a way analogous to belief bias. Experiment 3 showed that this guaranteed relevance can be suppressed, with participants reverting to the defective conditional. Experiment 4 showed that this pattern can be partly explained by a measure of inference strength. This pattern supports our theory’s “principle of relevant inference” and “principle of bounded inference,” highlighting the dual processing characteristics of the inferential connection. [ABSTRACT FROM AUTHOR]

Orthogonal linear regression is a standard statistical method which is used to fit a line to a scatter plot of data points ( x i , y i ) in situations where both variables have errors. Until now the US American R.J. Adcock has been considered to be the first who published this method, which is based on the method of least squares. We show that Julius Weisbach, professor of mathematics and engineering at Bergakademie Freiberg in Saxony (Germany) had already published in 1840 a paper in which the method is fully described and applied to an interesting problem. We discuss the context of his discovery in order to understand the type of problems mining surveyors faced in that time and why the use of this method was found to be relevant in geodesy. Weisbach's method of solution is then explained in all detail. We show that he implicitly used the method of least squares, but presented the solution in terms of geometrical arguments adapted to the readership of the journal in which he published. [ABSTRACT FROM AUTHOR]

As the vibro-acoustic requirements of modern products become more stringent, the need for robust identification methods increases proportionally. Sometimes the identification of a component is greatly complicated by the presence of a supporting structure that cannot be removed during testing. This is where substructure decoupling finds its main applications. However, despite some recent advances in substructure decoupling, the number of successful applications has so far been limited. The main reason for this is the poor conditioning of the problem that tends to amplify noise and other measurement errors. This paper proposes a new approach that uses a modal model to filter the experimental frequency response functions (FRFs). This can reduce the impact of noise and mass loading considerably for decoupling applications and decrease the quality requirements for experimental data. Furthermore, based on the uncertainty of the observed eigenfrequencies, an arbitrary number of consistent (all FRFs exhibit exactly the same poles) FRF matrices can be generated that are all contained within the variation of the original measurement. This way, the variation that is observed within the measurement is taken into account. The result is a distribution of decoupled FRFs of which the average can be used as the decoupled FRF set while the spread on the results highlights the sensitivity or reliability of the obtained results. After briefly reintroducing the theory of FRF-based substructure decoupling, the main problems in decoupling are summarized. Afterwards, the new methodology is presented and tested on both numerical and experimental cases. [ABSTRACT FROM AUTHOR]

Abstract: The paper shows that the fourth cumulant of a finite mixture distribution might be decomposed into the mean of the components’ fourth cumulants and the fourth cumulant of the components’ means, when the mixture’s components have the same second and third cumulants. Statistical applications include robustness properties of likelihood-based testing procedures and kurtosis-based projection methods. Practical relevance of theoretical results in the paper are illustrated with two well-known data sets. [Copyright &y& Elsevier]

Abstract: High dimensional covariance estimation is an important topic in contemporary multivariate statistics and has recently received much attention in the mathematical statistics literature. The work of Bickel and Levina (2008) [2] introduces a general approach to such estimation problems in a large class of models: banding of the sample covariance matrix. Bickel and Levina show that banded estimators are consistent in the operator norm as the dimension of the covariance matrix, , and the sample size, , both go to infinity. Critically, these estimators rely on knowing the order of the covariates apriori before banding can be applied. A rigorous framework for order recovery is however not available in the literature. In this paper, we propose a novel framework and methodology that can be used to recover covariate order in general classes of banded models. Such models can also be framed as autoregressive processes, which in turn fall within the class of graphical models. We show that recovering covariate order is intimately related to minimizing functionals on the symmetric group. Indeed, an important contribution of the paper is a result showing that the natural time order in such an autoregressive process has the property that over all orderings of covariates, it minimizes the sum of the diagonals of the Cholesky decomposition, of both the covariance and the inverse covariance matrix. This result lays the foundation for the ensuing statistical methodology developed in this paper: an efficient algorithm called the Best Permutation Algorithm (BPA). The BPA can recover the natural order of variables in autoregressive models at the rate of , which is the same rate that the covariance matrix can be estimated if the natural time order were known. Hence the BPA yields the oracle rate. Moreover, the computational complexity of the BPA is proved to be polynomial in the number of variables, , and hence allows for an efficient search over the full permutation group on letters, a group whose size is super-exponential in . The methodology is also successfully illustrated on numerical examples. [Copyright &y& Elsevier]

Abstract: For many of the unsolved problems concerning cycles and matchings in graphs it is known that it is sufficient to prove them for snarks, the class of non-trivial 3-regular graphs which cannot be 3-edge coloured. In the first part of this paper we present a new algorithm for generating all non-isomorphic snarks of a given order. Our implementation of the new algorithm is 14 times faster than previous programs for generating snarks, and 29 times faster for generating weak snarks. Using this program we have generated all non-isomorphic snarks on vertices. Previously lists up to vertices have been published. In the second part of the paper we analyze the sets of generated snarks with respect to a number of properties and conjectures. We find that some of the strongest versions of the cycle double cover conjecture hold for all snarks of these orders, as does Jaegerʼs Petersen colouring conjecture, which in turn implies that Fulkersonʼs conjecture has no small counterexamples. In contrast to these positive results we also find counterexamples to eight previously published conjectures concerning cycle coverings and the general cycle structure of cubic graphs. [Copyright &y& Elsevier]

Liu et al. (2015) proposed a novel link-free procedure for testing whether two multi-index models share identical indices via the sufficient dimension reduction approach. However, their method can only be applied to data with two populations. In practice, we often deal with situations where the same variables are being measured on objects from three or more groups, and we would like to know how similar these groups are with respect to some overall features. In this paper, we propose a link-free method which could test if three or more multi-index models share the same indices. The asymptotic properties of our test statistic are developed. Numerical studies and a real data analysis are conducted to illustrate the performance of our method. [ABSTRACT FROM AUTHOR]

For general repeated measures designs the Wald-type statistic (WTS) is an asymptotically valid procedure allowing for unequal covariance matrices and possibly non-normal multivariate observations. The drawback of this procedure is its poor performance for small to moderate samples, i.e., decisions based on the WTS may become quite liberal. It is the aim of the present paper to improve the small-sample behavior of the WTS by means of a novel permutation procedure. In particular, it is shown that a permutation version of the WTS inherits its good large-sample properties while yielding a very accurate finite-sample control of the type-I error as shown in extensive simulations. Moreover, the new permutation method is motivated by a practical data set of a split plot design with a factorial structure on the repeated measures. [ABSTRACT FROM AUTHOR]

This paper proposes a new conditional kernel CCA (canonical correlation analysis) algorithm and exploits statistical consistency of it via modified Tikhonov regularization scheme, which is a continuous study of [11] . A new measure which characterizes consistency of learning ability is discussed based on the notion of distance between feature subspaces. The consistency analysis is conducted under the assumptions of normalized cross-covariance operators, which is mild and can be constructed by means of mean square contingency. Meantime, the relationship between this new measure and previous consistency scheme is investigated. Furthermore, we study conditional kernel CCA in a more general scenario by means of the trace operator. [ABSTRACT FROM AUTHOR]

Abstract: This paper quantifies the form of the asymptotic covariance matrix of the sample autocovariances in a multivariate stationary time series—the classic Bartlett formula. Such quantification is useful in many statistical inferences involving autocovariances. While joint asymptotic normality of the sample autocovariances is well-known in univariate settings, explicit forms of the asymptotic covariances have not been investigated in the general multivariate non-Gaussian case. We fill this gap by providing such an analysis, bookkeeping all skewness terms. Additionally, following a recent univariate paper by Francq and Zakoian, we consider linear processes driven by non-independent errors, a feature that permits consideration of multivariate GARCH processes. [Copyright &y& Elsevier]

Abstract: Multivariate isotonic regression theory plays a key role in the field of statistical inference under order restriction for vector valued parameters. Two cases of estimating multivariate normal means under order restricted set are considered. One case is that covariance matrices are known, the other one is that covariance matrices are unknown but are restricted by partial order. This paper shows that when covariance matrices are known, the estimator given by this paper always dominates unrestricted maximum likelihood estimator uniformly, and when covariance matrices are unknown, the plug-in estimator dominates unrestricted maximum likelihood estimator under the order restricted set of covariance matrices. The isotonic regression estimators in this paper are the generalizations of plug-in estimators in unitary case. [Copyright &y& Elsevier]

Abstract: The BLU properties of OLS estimators under known assumptions have encouraged the widespread use of OLS multivariate regression analysis in many empirical studies that are based upon a conceptual model of a single explanatory equation. However, such a model may well be an imperfect empirical approximation to the valid underlying conceptual model, that may well contain several important additional inter-relationships between the relevant variables. In this paper, we examine the conditions under which we can predict the direction of the resultant endogeneity bias that will prevail in the OLS asymptotic parameter estimates for any given endogenous or predetermined variable, and the extent to which we can rely upon simple heuristics in this process. We also identify the underlying structural parameters to which the magnitude of the endogeneity bias is sensitive. The importance of such sensitivity analysis has been underlined by an increasing awareness of the inability of standard diagnostic tests to shed light upon the extent of the endogeneity bias, rather than upon merely its existence. The paper examines the implications of the analysis for statistical inferences about the true value of the regression coefficients and the validity of associated -statistics. [Copyright &y& Elsevier]

Abstract: The paper is about the asymptotic properties of the maximum likelihood estimator for the extreme value index. Under the second order condition, Drees et al. [H. Drees, A. Ferreira, L. de Haan, On maximum likelihood estimation of the extreme value index, Ann. Appl. Probab. 14 (2004) 1179–1201] proved asymptotic normality for any solution of the likelihood equations (with shape parameter ) that is not too far off the real value. But they did not prove that there is a solution of the equations satisfying the restrictions. In this paper, the existence is proved, even for . The proof just uses the domain of attraction condition (first order condition), not the second order condition. It is also proved that the estimator is consistent. When the second order condition is valid, following the current proof, the existence of a solution satisfying the restrictions in the above-cited reference is a direct consequence. [Copyright &y& Elsevier]

Abstract: In this paper we examine the orders of vertex-transitive self-complementary uniform hypergraphs. In particular, we prove that if there exists a vertex-transitive self-complementary -uniform hypergraph of order , where or and , then the highest power of any prime dividing must be congruent to 1 modulo . We show that this necessary condition is also sufficient in many cases–for example, for a prime power, and for and odd–thus generalizing the result on vertex-transitive self-complementary graphs of Rao and Muzychuk. We also give sufficient conditions for the existence of vertex-transitive self-complementary uniform hypergraphs in several other cases. Since vertex-transitive self-complementary uniform hypergraphs are equivalent to a certain kind of large sets of -designs, the results of the paper imply the corresponding results in design theory. [Copyright &y& Elsevier]

Abstract: In most papers establishing consistency for learning algorithms it is assumed that the observations used for training are realizations of an i.i.d. process. In this paper we go far beyond this classical framework by showing that support vector machines (SVMs) only require that the data-generating process satisfies a certain law of large numbers. We then consider the learnability of SVMs for -mixing (not necessarily stationary) processes for both classification and regression, where for the latter we explicitly allow unbounded noise. [Copyright &y& Elsevier]

Based on data mining technology, this paper incorporates Bayesian networks to examine ecosystem data in order to investigate the static and dynamic benefits of returning farmland to forests and ecological compensation. The restricted network structure is suggested to reduce training costs and simplify model structure. Simultaneously, in order to increase prediction accuracy over a single model, ensemble learning is utilized to train multiple models to solve the same problem. Furthermore, based on data mining, this article explores the ecosystem's development purpose, constituent elements, and static framework, illustrates its operation and evolution mechanism, and constructs an evaluation system for returning farmland to forest and ecological compensation. Finally, this article incorporates current situation to determine the static and dynamic benefits, and then systematically verifies it using experiments and mathematical statistics. The research findings indicate that the impact of the framework built in this paper meets the standards of the construction model which could be used in practice. • Evaluate the static benefits and dynamic effects of returning farmland. • Analyzed ecological compensation benefits & established dynamic cycle compensation model. • Illustrates operation & evolution mechanism, and constructs an evaluation system for returning farmland. [ABSTRACT FROM AUTHOR]

Environmental policies and guidelines often specify standards for environmental indicators. The first part of this paper argues that, where compliance with these standards is assessed with the help of statistical inference, an inference employing a three-alternatives decision rule can provide more sensible feedback to environmental managers for precautionary decision-making. The second part of the paper shows how a three-alternatives statistical inference about compliance with a percentile standard might be applied to a small number of observations using a non-parametric binomial interval. This interval expression of uncertainty results in the sample size requirements for various percentile ranks becoming explicit. [Copyright &y& Elsevier]

Abstract: A new class of bivariate distributions is presented in this paper. The procedure used in this paper is based on a latent random variable with exponential distribution. The model introduced here is of Marshall–Olkin type. A mixture of the proposed bivariate distributions is also discussed. The results obtained here generalize those of the bivariate exponential distribution present in the literature. [Copyright &y& Elsevier]

Abstract: In risk management, ignoring the dependence among various types of claims often results in over-estimating or under-estimating the ruin probabilities of a portfolio. This paper focuses on three commonly used ruin probabilities in multivariate compound risk models, and using the comparison methods shows how some ruin probabilities increase, whereas the others decrease, as the claim dependence grows. The paper also presents some computable bounds for these ruin probabilities, which can be calculated explicitly for multivariate phase-type distributed claims, and illustrates the performance of these bounds for the multivariate compound Poisson risk models with slightly or highly dependent Marshall–Olkin exponential claim sizes. [Copyright &y& Elsevier]

Abstract: In this paper we study the stability of transonic shocks in steady supersonic flow past a wedge. We take the potential flow equation as the mathematical model to describe the compressible flow. It is known that in generic case such a problem admits two possible location of shock, connecting the flow ahead it and behind it. They can be distinguished as supersonic–supersonic shock and supersonic–subsonic shock (or transonic shock). Both these possible shocks satisfy the Rankine–Hugoniot conditions and entropy condition. In this paper we prove that the transonic shock is also stable under perturbation of the coming flow provided the pressure at infinity is well controlled. [Copyright &y& Elsevier]

Abstract: In this paper we introduce a mathematical model of naming games. Naming games have been widely used within research on the origins and evolution of language. Despite the many interesting empirical results these studies have produced, most of this research lacks a formal elucidating theory. In this paper we show how a population of agents can reach linguistic consensus, i.e. learn to use one common language to communicate with one another. Our approach differs from existing formal work in two important ways: one, we relax the too strong assumption that an agent samples infinitely often during each time interval. This assumption is usually made to guarantee convergence of an empirical learning process to a deterministic dynamical system. Two, we provide a proof that under these new realistic conditions, our model converges to a common language for the entire population of agents. Finally the model is experimentally validated. [Copyright &y& Elsevier]

Abstract: The (univariate) -distribution and symmetric V.G. distribution are competing models [D.S. Madan, E. Seneta, The variance gamma (V.G.) model for share market returns, J. Business 63 (1990) 511–524; T.W. Epps, Pricing Derivative Securities, World Scientific, Singapore, 2000 (Section 9.4)] for the distribution of log-increments of the price of a financial asset. Both result from scale-mixing of the normal distribution. The analogous matrix variate distributions and their characteristic functions are derived in the sequel and are dual to each other in the sense of a simple Duality Theorem. This theorem can thus be used to yield the derivation of the characteristic function of the t-distribution and is the essence of the idea used by Dreier and Kotz [A note on the characteristic function of the -distribution, Statist. Probab. Lett. 57 (2002) 221–224]. The present paper generalizes the univariate ideas in Section 6 of Seneta [Fitting the variance-gamma (VG) model to financial data, stochastic methods and their applications, Papers in Honour of Chris Heyde, Applied Probability Trust, Sheffield, J. Appl. Probab. (Special Volume) 41A (2004) 177–187] to the general matrix generalized inverse gaussian (MGIG) distribution. [Copyright &y& Elsevier]

Abstract: The main purpose of this paper is to propose a new fault feature extraction approach based on empirical mode decomposition (EMD) method and autoregressive (AR) model for roller bearings. AR model is an effective approach to extract the fault feature of the vibration signals and the fault pattern can be identified directly by the extracted fault features without establishing the mathematical model and studying the fault mechanism of the system. However, AR model can only be applied to stationary signals, while the fault vibration signals of a roller bearing are non-stationary. Aiming at this problem, in this paper, the EMD method is used as a pretreatment to decompose the non-stationary vibration signal of a roller bearing into a number of intrinsic mode function (IMF) components which are stationary, then the AR model of each IMF component can be established. The AR parameters and the remnant''s variance of the AR models of each IMF components are regarded as the feature vectors. The distance criterion function is used to identify the condition and fault pattern of a roller bearing. Experimental analysis results show that the roller bearing fault features can be extracted by the proposed approach effectively. [Copyright &y& Elsevier]

Abstract: The early work of Zellner on the multivariate Student- linear model has been extended to Bayesian inference for linear models with dependent non-normal error terms, particularly through various papers by Osiewalski, Steel and coworkers. This article provides a full Bayesian analysis for a spherical linear model. The density generator of the spherical distribution is here allowed to depend both on the precision parameter and on the regression coefficients . Another distinctive aspect of this paper is that proper priors for the precision parameter are discussed. The normal-chi-squared family of prior distributions is extended to a new class, which allows the posterior analysis to be carried out analytically. On the other hand, a direct joint modelling of the data vector and of the parameters leads to conjugate distributions for the regression and the precision parameters, both individually and jointly. It is shown that some model specifications lead to Bayes estimators that do not depend on the choice of the density generator, in agreement with previous results obtained in the literature under different assumptions. Finally, the distribution theory developed to tackle the main problem is useful on its own right. [Copyright &y& Elsevier]

Abstract: Cyclical neutropenia is a dynamical disease of the hematopoietic system marked by an oscillation in circulating leukocyte (e.g. neutrophil) numbers to near zero levels and then back to normal. This oscillation is also mirrored in the platelets and reticulocytes which oscillate with the same period. Cyclical neutropenia has an animal counterpart in the grey collie. Using the mathematical model of the hematopoietic system of Colijn and Mackey [A mathematical model of hematopoiesis: I. Periodic chronic myelogenous leukemia. Companion paper to the present paper.] we have determined what parameters are necessary to mimic laboratory and clinical data on untreated grey collies and humans, and also what changes in these parameters are necessary to fit data during treatment with granulocyte colony stimulating factor (G-CSF). Compared to the normal steady-state values, we found that the major parameter changes that mimic untreated cyclical neutropenia correspond to a decreased amplification (increased apoptosis) within the proliferating neutrophil precursor compartment, and a decrease in the maximal rate of re-entry into the proliferative phase of the stem cell compartment. For the data obtained during G-CSF treatment, good fits were obtained only when parameters were altered that would imply that G-CSF led to higher amplification (lower rate of apoptosis) in the proliferating neutrophil precursors, and a elevated rate of differentiation into the neutrophil line. [Copyright &y& Elsevier]

Abstract: In a recent paper [P. Bates, A. Chmaj, A discrete convolution model for phase transition, Arch. Rational Mech. Anal. 150 (1999) 281–305], a discrete convolution model for Ising-like phase transition has been derived, and the existence, uniqueness of traveling waves and stability of stationary solution have been studied. This nonlocal model describes -gradient flow for a Helmholts free energy functional with general range interaction. In this paper, by using the comparison principle and the squeezing technique, we prove that the traveling wavefronts with nonzero speed is globally asymptotic stable with phase shift. [Copyright &y& Elsevier]

Abstract: It is well understood that the orientation of clasts within a deposit can yield information on site formation processes. Although the term clast usually refers to the natural component of a deposit, the artifactual and faunal components are also known to be sensitive to site formation processes. There are a number of ways in which orientations can be recorded and analyzed, and this paper describes how orientations can be recorded as part of piece proveniencing artifacts with a total station. After the method is described, the calculation, statistical analysis and presentation of artifact orientations are considered. The final part of the paper presents orientation data from Pech de l''Azé IV recorded and analyzed in this way. The example demonstrates the effectiveness of the total station method of recording orientations, their sensitivity to site formation processes, and the importance of using multiple methods of data analysis and presentation to assess and interpret patterning. [Copyright &y& Elsevier]

Abstract: Li and Chen (J. Amer. Statist. Assoc. 80 (1985) 759) proposed a method for principal components using projection-pursuit techniques. In classical principal components one searches for directions with maximal variance, and their approach consists of replacing this variance by a robust scale measure. Li and Chen showed that this estimator is consistent, qualitative robust and inherits the breakdown point of the robust scale estimator. We complete their study by deriving the influence function of the estimators for the eigenvectors, eigenvalues and the associated dispersion matrix. Corresponding Gaussian efficiencies are presented as well. Asymptotic normality of the estimators has been treated in a paper of Cui et al. (Biometrika 90 (2003) 953), complementing the results of this paper. Furthermore, a simple explicit version of the projection-pursuit based estimator is proposed and shown to be fast to compute, orthogonally equivariant, and having the maximal finite-sample breakdown point property. We will illustrate the method with a real data example. [Copyright &y& Elsevier]

In this paper two advanced frequency-domain estimators, the advanced least squares complex frequency estimator and the advanced maximum likelihood estimator will be presented to deal with short and noisy data records for applications in the field of modal analysis. This requires adapted frequency-domain estimators designed to handle large amount of data in a reasonable amount of time. The identification techniques proposed in this paper start from frequency response functions and take into account leakage and transient phenomena. Special attention is paid to reduce the computation time as well as the memory requirements. Finally, the proposed techniques are evaluated using both simulated and experimental data. Both simulations and measurements show that there is an improvement on the accuracy of the estimated models. [Copyright &y& Elsevier]

The paper states how to stabilize the periodic motions of a linear, non-positively damped system of single degree of freedom through the use of delayed state feedback. The study indicates that the delayed state feedback works in certain frequency ranges. For a linear undamped system of single degree of freedom, the delayed displacement feedback is able to stabilize almost all periodic motions of the system provided that their fundamental frequencies are higher than the natural frequency of the system, but it only works in a series of narrower and narrower frequency bands lower than the natural frequency. The introduction of delayed velocity feedback can remarkably enlarge the working frequency ranges of delayed displacement feedback. However, even the delayed state feedback cannot stabilize a periodic motion if the corresponding period is an integral multiple of the natural period of system.The criteria of stability switches prove to be a powerful tool to analyze the stabilization problem of a linear system. However, the stabilization of a linear undamped system of single degree of freedom with delayed velocity feedback only is a degenerate case where the available criteria of stability switches fail to offer any useful information. A detailed study in the paper reveals the complexity of the degenerated case, where the stabilization conditions can be identified according to the second order derivative of the real part of an arbitrary characteristic root. [Copyright &y& Elsevier]

For a subgroup T of a group G( ∘ ), letL ∘ (T) andR ∘ (T) denote the sets of all left and right cosets, respectively. This paper is concerned with finite groups G( ∘ ) andG ( * ), where the places in which the Cayley tables of the two groups differ is determined by subgroups S < H ≤ G( ∘ ), such that |H : S | = 2, in the sense that for all (α, β) ∈ L ∘ (H) × R ∘ (H) one can find (α0,β0 ) ∈ L ∘ (S) × R ∘ (S) so thatα0 ⊆ α and β0 ⊆ β , and so that x ∘ y ≠ = x * y holds for (x, y) ∈ α × β if and only if (x, y) ∈ α0 × β0. GivenG ( ∘ ) and G( * ), there can be multiple choices ofS and H and it is proved in the paper that there always exists a choice for which S is a normal subgroup of both G( ∘ ) and G( * ), andG ( ∘ ) / S = G ( * ) / S is either cyclic or dihedral (where the latter includes Klein’s four-element group). The specification of S and H is precise enough to permit a detailed description of the set of products for which ∘ and * differ and of the way in which they differ and, moreover, to permit the derivation of G( * ) fromG ( ∘ ) (without knowing G( * ) in advance). [Copyright &y& Elsevier]

We show how it is possible to assess the rate of convergence in the Gaussian approximation of triangular arrays of U -statistics, built from wavelets coefficients evaluated on a spherical Poisson field of arbitrary dimension. For this purpose, we exploit the Stein–Malliavin approach introduced in the seminal paper by Peccati, Solé, Taqqu and Utzet (2011); we focus in particular on statistical applications covering evaluation of variance in non-parametric density estimation and Sobolev tests for uniformity. [ABSTRACT FROM AUTHOR]

This paper is concerned with the development of a general methodology for identifying mechanical sources from prior local information on both their nature and location over the studied structure. For this purpose, the formulation of the identification problem is derived from the Bayesian statistics, that provides a flexible way to account for local a priori on the distribution of sources. Practically, the resulting optimization problem can be seen as a group generalized Tikhonov regularization, that is solved in an iterative manner. The main features of the proposed identification method are illustrated with both numerical and experimental examples. In particular, it is shown that properly exploiting the local spatial information drastically improves the quality of the source identification. [ABSTRACT FROM AUTHOR]

Estimators of partial parameters in general linear models involve some complicated operations of the submatrices in the given matrices and their generalized inverses in the models. In this case, more efforts are needed to find variety of properties hidden behind these estimators. In this paper, we use some new analytical tools in matrix theory to investigate the connections between the ordinary least-squares estimators and the best linear unbiased estimators of the whole and partial unknown parameters in general linear model with restrictions. In particular, we derive necessary and sufficient conditions for the ordinary least-squares estimators to be the best linear unbiased estimators of the whole and partial unknown parameters in the model. [ABSTRACT FROM AUTHOR]

Given a probability measure μ , on the space of strictly positive densities M μ , we construct a topological manifold on which the elements are connected by κ -exponential models in the form q = exp κ ⁡ ( u ⊖ κ K p , κ ( u ) ) p , where exp κ ⁡ ( x ) = ( 1 + κ 2 x 2 + κ x ) 1 / κ , x ⊖ κ y = x 1 + κ 2 y 2 − y 1 + κ 2 x 2 , p , q ∈ M μ , and their local representations are elements of an Orlicz space, i.e. the manifold is modeled on Orlicz spaces. Parameter k is the G. Kaniadakis parameter for κ -deformed exponentials which is strongly relevant to relativity and statistical complex models in statistical mechanics. Functional K p , κ is the deformed counterpart of the cumulant mapping and satisfies that, if κ → 0 , we obtain the usual cumulant functional of the exponential manifold; moreover in this limit case the exponential manifold constructed by Pistone and Sempi is recovered. In the context of deformed exponentials, we prove that the function ϕ κ ( ⋅ ) = cosh κ ⁡ ( ⋅ ) − 1 , where cosh κ ⁡ ( x ) is the κ -deformed hyperbolic cosine, is a Young function and generates the Orlicz space on which the κ -exponential manifold is modeled, namely L ϕ κ ( p ⋅ μ ) . This construction differs from the one made by Pistone on the paper κ-exponential models from the geometrical viewpoint , since this last one is based on divergence functionals and modeled on Lebesgue spaces L 1 / κ ( p ⋅ μ ) . The use of κ -deformed models is interesting since they generalize the exponential models and extend them to non-additive systems. [ABSTRACT FROM AUTHOR]

In this paper, we present a new reversible data hiding method based on histogram shifting using localization. Our proposed method selects peak point as the reference point, then uses the two neighboring points of the peak point to achieve secret data embedding based on histogram shifting and the peak point keeps unchanged. In the extraction end, we no longer need the key information about the peak point, we can directly find the peak point from the histogram to extract the secret data. We also exploit the localization to make the histogram of embedded cover image become almost the same as the histogram of the original cover image. The embedding capacity is also increased rapidly by the localization with multilayer embedding. Experimental results show that our proposed method is effective and superior. [ABSTRACT FROM AUTHOR]

Inference for variance components in linear mixed models of ANOVA type, including estimation and testing, has been investigated when the number of fixed effects is fixed. However, for high-dimensional data, this number is large and would be regarded as a divergent value as the sample size goes to infinity. In this paper, existing tests are extended to handle this problem with a sparse model structure. To avoid the impact from insignificant fixed effects, the proposed tests are post-selection-based with an orthogonality-based selection of SCAD type applied to selecting significant fixed effects into working model. The selection and estimation of fixed effects are under the assumption on the existence of second order moments for errors. Two types of tests for random effects are considered and some new insights are obtained. The proposed tests are distribution-free, though they request the existence of the fourth moments of random effects and errors. The proposed methods are illustrated by simulation studies and a real data analysis. [ABSTRACT FROM AUTHOR]

The class of Dirichlet random vectors is central in numerous probabilistic and statistical applications. The main result of this paper derives the exact tail asymptotics of the aggregated risk of powers of Dirichlet random vectors when the radial component has df in the Gumbel or the Weibull max-domain of attraction. We present further results for the joint asymptotic independence and the max–sum equivalence. [ABSTRACT FROM AUTHOR]

Classification arises in a wide range of applications. A variety of statistical tools have been developed for learning classification rules from data. Understanding of their relative merits and comparisons help users to choose a proper method in practice. This paper focuses on theoretical comparison of model-based classification methods in statistics with algorithmic methods in machine learning in terms of the error rate. Extending Efron’s comparison of logistic regression with linear discriminant analysis (LDA) under the normal setting, we contrast such algorithmic methods as the support vector machine (SVM) and boosting with the LDA and logistic regression and study their relative efficiencies in reducing the error rate based on the limiting behavior of the classification boundary of each method. We show that algorithmic methods are generally less effective than model-based methods in the normal setting. In particular, loss of efficiency in error rate is typically about 33% to 60% for the SVM and 50% to 80% for boosting when compared to the LDA. However, a smooth variant of the SVM is shown to be even more efficient than logistic regression. In addition to the theoretical study, we present results from numerical experiments under various settings for comparisons of finite-sample performance and robustness to mislabeling and model misspecification. [ABSTRACT FROM AUTHOR]

Statistical methods for functional data are of interest for many applications. In this paper, we prove a central limit theorem for random variables taking their values in a Hilbert space. The random variables are assumed to be weakly dependent in the sense of near epoch dependence, where the underlying process fulfills some mixing conditions. As parametric inference in an infinite dimensional space is difficult, we show that the nonoverlapping block bootstrap is consistent. Furthermore, we show how these results can be used for degenerate von Mises-statistics. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the variable selection for semiparametric varying coefficient partially linear models with longitudinal data. A new variable selection procedure is proposed based on the combination of the basis function approximations and quadratic inference functions. The proposed procedure simultaneously selects significant variables in the parametric components and the nonparametric components. With appropriate selection of the tuning parameters, we establish the consistency and asymptotic normality of the resulting estimators. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed variable selection procedure. We further illustrate the proposed procedure by an application. [ABSTRACT FROM AUTHOR]