Relevant bibliographies by topics / Stationary random process

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Stationary random process'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Stationary random process"

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Chung, Jaeyoung, Dohan Kim, and Eun Gu Lee. "Stationary hyperfunctional random process." Complex Variables and Elliptic Equations 59, no. 11 (March 14, 2013): 1547–58. http://dx.doi.org/10.1080/17476933.2012.757309.

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Lacaze, Pr B. "A note about stationary process random sampling." Statistics & Probability Letters 31, no. 2 (December 1996): 133–37. http://dx.doi.org/10.1016/s0167-7152(96)00024-7.

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Menh, Nguyen Cao, and Tran Duong Tri. "Simulation of stationary, non-normal random process." Vietnam Journal of Mechanics 14, no. 4 (December 31, 1992): 19–26. http://dx.doi.org/10.15625/0866-7136/10240.

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Simulation of stationary random processes specified by spectral density function and NonNormal probability density function has been rarely studied, though these processes are often met in both theory and practice. In this paper we consider a simulation method which can apply to the mention-above processes. The main idea based on the simulation of the Normal process and approximation of functions in the space of quadratic ally integrable functions. The numerical program for illustration of the method is written by Turbo - Pascal language.
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Kella, Offer, Onno Boxma, and Michel Mandjes. "A Lévy Process Reflected at a Poisson Age Process." Journal of Applied Probability 43, no. 01 (March 2006): 221–30. http://dx.doi.org/10.1017/s0021900200001480.

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We consider a Lévy process with no negative jumps, reflected at a stochastic boundary that is a positive constant multiple of an age process associated with a Poisson process. We show that the stability condition for this process is identical to the one for the case of reflection at the origin. In particular, there exists a unique stationary distribution that is independent of initial conditions. We identify the Laplace-Stieltjes transform of the stationary distribution and observe that it satisfies a decomposition property. In fact, it is a sum of two independent random variables, one of which has the stationary distribution of the process reflected at the origin, and the other the stationary distribution of a certain clearing process. The latter is itself distributed as an infinite sum of independent random variables. Finally, we discuss the tail behavior of the stationary distribution and in particular observe that the second distribution in the decomposition always has a light tail.
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Kella, Offer, Onno Boxma, and Michel Mandjes. "A Lévy Process Reflected at a Poisson Age Process." Journal of Applied Probability 43, no. 1 (March 2006): 221–30. http://dx.doi.org/10.1239/jap/1143936255.

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We consider a Lévy process with no negative jumps, reflected at a stochastic boundary that is a positive constant multiple of an age process associated with a Poisson process. We show that the stability condition for this process is identical to the one for the case of reflection at the origin. In particular, there exists a unique stationary distribution that is independent of initial conditions. We identify the Laplace-Stieltjes transform of the stationary distribution and observe that it satisfies a decomposition property. In fact, it is a sum of two independent random variables, one of which has the stationary distribution of the process reflected at the origin, and the other the stationary distribution of a certain clearing process. The latter is itself distributed as an infinite sum of independent random variables. Finally, we discuss the tail behavior of the stationary distribution and in particular observe that the second distribution in the decomposition always has a light tail.
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Subba Rao, Suhasini. "On some nonstationary, nonlinear random processes and their stationary approximations." Advances in Applied Probability 38, no. 4 (December 2006): 1155–72. http://dx.doi.org/10.1017/s000186780000149x.

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In this paper our object is to show that a certain class of nonstationary random processes can locally be approximated by stationary processes. The class of processes we are considering includes the time-varying autoregressive conditional heteroscedastic and generalised autoregressive conditional heteroscedastic processes, amongst others. The measure of deviation from stationarity can be expressed as a function of a derivative random process. This derivative process inherits many properties common to stationary processes. We also show that the derivative processes obtained here have alpha-mixing properties.
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Daley, D. J., T. Rolski, and R. Vesilo. "Long-Range Dependence in a Cox Process Directed by a Markov Renewal Process." Journal of Applied Mathematics and Decision Sciences 2007 (November 22, 2007): 1–15. http://dx.doi.org/10.1155/2007/83852.

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A Cox process NCox directed by a stationary random measure ξ has second moment var NCox(0,t]=E(ξ(0,t])+var ξ(0,t], where by stationarity E(ξ(0,t])=(const.)t=E(NCox(0,t]), so long-range dependence (LRD) properties of NCox coincide with LRD properties of the random measure ξ. When ξ(A)=∫AνJ(u)du is determined by a density that depends on rate parameters νi(i∈𝕏) and the current state J(⋅) of an 𝕏-valued stationary irreducible Markov renewal process (MRP) for some countable state space 𝕏 (so J(t) is a stationary semi-Markov process on 𝕏), the random measure is LRD if and only if each (and then by irreducibility, every) generic return time Yjj(j∈X) of the process for entries to state j has infinite second moment, for which a necessary and sufficient condition when 𝕏 is finite is that at least one generic holding time Xj in state j, with distribution function (DF)\ Hj, say, has infinite second moment (a simple example shows that this condition is not necessary when 𝕏 is countably infinite). Then, NCox has the same Hurst index as the MRP NMRP that counts the jumps of J(⋅), while as t→∞, for finite 𝕏, var NMRP(0,t]∼2λ2∫0t𝒢(u)du, var NCox(0,t]∼2∫0t∑i∈𝕏(νi−ν¯)2ϖiℋi(t)du, where ν¯=∑iϖiνi=E[ξ(0,1]], ϖj=Pr{J(t)=j},1/λ=∑jpˇjμj, μj=E(Xj), {pˇj} is the stationary distribution for the embedded jump process of the MRP, ℋj(t)=μi−1∫0∞min(u,t)[1−Hj(u)]du, and 𝒢(t)∼∫0tmin(u,t)[1−Gjj(u)]du/mjj∼∑iϖiℋi(t) where Gjj is the DF and mjj the mean of the generic return time Yjj of the MRP between successive entries to the state j. These two variances are of similar order for t→∞ only when each ℋi(t)/𝒢(t) converges to some [0,∞]-valued constant, say, γi, for t→∞.
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Pleshakov, Ruslan V. "Simulation of non-stationary event flow with a nested stationary component." Russian Family Doctor 28, no. 1 (December 15, 2020): 35–48. http://dx.doi.org/10.17816/rfd10640.

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A method for constructing an ensemble of time series trajectories with a nonstationary flow of events and a non-stationary empirical distribution of the values of the observed random variable is described. We consider a special model that is similar in properties to some real processes, such as changes in the price of a financial instrument on the exchange. It is assumed that a random process is represented as an attachment of two processes - stationary and non-stationary. That is, the length of a series of elements in the sequence of the most likely event (the most likely price change in the sequence of transactions) forms a non-stationary time series, and the length of a series of other events is a stationary random process. It is considered that the flow of events is non-stationary Poisson process. A software package that solves the problem of modeling an ensemble of trajectories of an observed random variable is described. Both the values of a random variable and the time of occurrence of the event are modeled. An example of practical application of the model is given.
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Pleshakov, Ruslan V. "Simulation of non-stationary event flow with a nested stationary component." Russian Family Doctor 28, no. 1 (December 15, 2020): 35–48. http://dx.doi.org/10.17816/rfd10645.

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A method for constructing an ensemble of time series trajectories with a nonstationary flow of events and a non-stationary empirical distribution of the values of the observed random variable is described. We consider a special model that is similar in properties to some real processes, such as changes in the price of a financial instrument on the exchange. It is assumed that a random process is represented as an attachment of two processes - stationary and non-stationary. That is, the length of a series of elements in the sequence of the most likely event (the most likely price change in the sequence of transactions) forms a non-stationary time series, and the length of a series of other events is a stationary random process. It is considered that the flow of events is non-stationary Poisson process. A software package that solves the problem of modeling an ensemble of trajectories of an observed random variable is described. Both the values of a random variable and the time of occurrence of the event are modeled. An example of practical application of the model is given.
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Pleshakov, Ruslan V. "Simulation of non-stationary event flow with a nested stationary component." Discrete and Continuous Models and Applied Computational Science 28, no. 1 (December 15, 2020): 35–48. http://dx.doi.org/10.22363/2658-4670-2020-28-1-35-48.

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A method for constructing an ensemble of time series trajectories with a nonstationary flow of events and a non-stationary empirical distribution of the values of the observed random variable is described. We consider a special model that is similar in properties to some real processes, such as changes in the price of a financial instrument on the exchange. It is assumed that a random process is represented as an attachment of two processes - stationary and non-stationary. That is, the length of a series of elements in the sequence of the most likely event (the most likely price change in the sequence of transactions) forms a non-stationary time series, and the length of a series of other events is a stationary random process. It is considered that the flow of events is non-stationary Poisson process. A software package that solves the problem of modeling an ensemble of trajectories of an observed random variable is described. Both the values of a random variable and the time of occurrence of the event are modeled. An example of practical application of the model is given.
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Dissertations / Theses on the topic "Stationary random process"

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Kandler, Anne, Matthias Richter, Scheidt Jürgen vom, Hans-Jörg Starkloff, and Ralf Wunderlich. "Moving-Average approximations of random epsilon-correlated processes." Universitätsbibliothek Chemnitz, 2004. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200401266.

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The paper considers approximations of time-continuous epsilon-correlated random processes by interpolation of time-discrete Moving-Average processes. These approximations are helpful for Monte-Carlo simulations of the response of systems containing random parameters described by epsilon-correlated processes. The paper focuses on the approximation of stationary epsilon-correlated processes with a prescribed correlation function. Numerical results are presented.
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Starkloff, Hans-Jörg, Matthias Richter, Scheidt Jürgen vom, and Ralf Wunderlich. "On the convergence of random functions defined by interpolation." Universitätsbibliothek Chemnitz, 2004. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200401293.

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In the paper we study sequences of random functions which are defined by some interpolation procedures for a given random function. We investigate the problem in what sense and under which conditions the sequences converge to the prescribed random function. Sufficient conditions for convergence of moment characteristics, of finite dimensional distributions and for weak convergence of distributions in spaces of continuous functions are given. The treatment of such questions is stimulated by an investigation of Monte Carlo simulation procedures for certain classes of random functions. In an appendix basic facts concerning weak convergence of probability measures in metric spaces are summarized.
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Tian, Peng. "Asymptotiques et fluctuations des plus grandes valeurs propres de matrices de covariance empirique associées à des processus stationnaires à longue mémoire." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1131/document.

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Les grandes matrices de covariance constituent certainement l’un des modèles les plus utiles pour les applications en statistiques en grande dimension, en communication numérique, en biologie mathématique, en finance, etc. Les travaux de Marcenko et Pastur (1967) ont permis de décrire le comportement asymptotique de la mesure spectrale de telles matrices formées à partir de N copies indépendantes de n observations d’une suite de variables aléatoires iid et sa convergence vers une distribution de probabilité déterministe lorsque N et n convergent vers l’infini à la même vitesse. Plus récemment, Merlevède et Peligrad (2016) ont démontré que dans le cas de grandes matrices de covariance issues de copies indépendantes d’observations d’un processus strictement stationnaire centré, de carré intégrable et satisfaisant des conditions faibles de régularité, presque sûrement, la distribution spectrale empirique convergeait étroitement vers une distribution non aléatoire ne dépendant que de la densité spectrale du processus sous-jacent. En particulier, si la densité spectrale est continue et bornée (ce qui est le cas des processus linéaires dont les coefficients sont absolument sommables), alors la distribution spectrale limite a un support compact. Par contre si le processus stationnaire exhibe de la longue mémoire (en particulier si les covariances ne sont pas absolument sommables), le support de la loi limite n'est plus compact et des études plus fines du comportement des valeurs propres sont alors nécessaires. Ainsi, cette thèse porte essentiellement sur l’étude des asymptotiques et des fluctuations des plus grandes valeurs propres de grandes matrices de covariance associées à des processus stationnaires à longue mémoire. Dans le cas où le processus stationnaire sous-jacent est Gaussien, l’étude peut être simplifiée via un modèle linéaire dont la matrice de covariance de population sous-jacente est une matrice de Toeplitz hermitienne. On montrera ainsi que dans le cas de processus stationnaires gaussiens à longue mémoire, les fluctuations des plus grandes valeurs propres de la grande matrice de covariance empirique convenablement renormalisées sont gaussiennes. Ce comportement indique une différence significative par rapport aux grandes matrices de covariance empirique issues de processus à courte mémoire, pour lesquelles les fluctuations de la plus grande valeur propre convenablement renormalisée suivent asymptotiquement la loi de Tracy-Widom. Pour démontrer notre résultat de fluctuations gaussiennes, en plus des techniques usuelles de matrices aléatoires, une étude fine du comportement des valeurs propres et vecteurs propres de la matrice de Toeplitz sous-jacente est nécessaire. On montre en particulier que dans le cas de la longue mémoire, les m plus grandes valeurs propres de la matrice de Toeplitz convergent vers l’infini et satisfont une propriété de type « trou spectral multiple ». Par ailleurs, on démontre une propriété de délocalisation de leurs vecteurs propres associés. Dans cette thèse, on s’intéresse également à l’universalité de nos résultats dans le cas du modèle simplifié ainsi qu’au cas de grandes matrices de covariance lorsque les matrices de Toeplitz sont remplacées par des matrices diagonales par blocs
Large covariance matrices play a fundamental role in the multivariate analysis and high-dimensional statistics. Since the pioneer’s works of Marcenko and Pastur (1967), the asymptotic behavior of the spectral measure of such matrices associated with N independent copies of n observations of a sequence of iid random variables is known: almost surely, it converges in distribution to a deterministic law when N and n tend to infinity at the same rate. More recently, Merlevède and Peligrad (2016) have proved that in the case of large covariance matrices associated with independent copies of observations of a strictly stationary centered process which is square integrable and satisfies some weak regularity assumptions, almost surely, the empirical spectral distribution converges weakly to a nonrandom distribution depending only on the spectral density of the underlying process. In particular, if the spectral density is continuous and bounded (which is the case for linear processes with absolutely summable coefficients), the limiting spectral distribution has a compact support. However, if the underlying stationary process exhibits long memory, the support of the limiting distribution is not compact anymore and studying the limiting behavior of the eigenvalues and eigenvectors of the associated large covariance matrices can give more information on the underlying process. This thesis is in this direction and aims at studying the asymptotics and the fluctuations of the largest eigenvalues of large covariance matrices associated with stationary processes exhibiting long memory. In the case where the underlying stationary process is Gaussian, the study can be simplified by a linear model whose underlying population covariance matrix is a Hermitian Toeplitz matrix. In the case of stationary Gaussian processes exhibiting long memory, we then show that the fluctuations of the largest eigenvalues suitably renormalized are Gaussian. This limiting behavior shows a difference compared to the one when large covariance matrices associated with short memory processes are considered. Indeed in this last case, the fluctuations of the largest eigenvalues suitably renormalized follow asymptotically the Tracy-Widom law. To prove our results on Gaussian fluctuations, additionally to usual techniques developed in random matrices analysis, a deep study of the eigenvalues and eigenvectors behavior of the underlying Toeplitz matrix is necessary. In particular, we show that in the case of long memory, the largest eigenvalues of the Toeplitz matrix converge to infinity and satisfy a property of “multiple spectral gaps”. Moreover, we prove a delocalization property of their associated eigenvectors. In this thesis, we are also interested in the universality of our results in the case of the simplified model and also in the case of large covariance matrices when the Toeplitz matrices are replaced by bloc diagonal matrices
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Thai, Anh-Thi Marie Noémie. "Processus de Fleming-Viot, distributions quasi-stationnaires et marches aléatoires en interaction de type champ moyen." Thesis, Paris Est, 2015. http://www.theses.fr/2015PESC1124/document.

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Dans cette thèse nous étudions le comportement asymptotique de systèmes de particules en interaction de type champ moyen en espace discret, systèmes pour lesquels l'interaction a lieu par l'intermédiaire de la mesure empirique. Dans la première partie de ce mémoire, nous nous intéressons aux systèmes de particules de type Fleming-Viot: les particules se déplacent indépendamment suivant une dynamique markovienne jusqu'au moment où l'une d'entre elles touche un état absorbant. A cet instant, la particule absorbée choisit uniformément une autre particule et saute sur sa position. L'ergodicité du processus est établie dans le cadre de marches aléatoires sur N avec dérive vers l'origine et pour une dynamique proche de celle du graphe complet. Pour ce dernier, nous obtenons une estimation quantitative de la convergence en temps long à l'aide de la courbure de Wasserstein. Nous montrons de plus la convergence de la distribution empirique stationnaire vers une unique distribution quasi-stationnaire, quand le nombre de particules tend vers l'infini. Dans la deuxième partie de ce mémoire, nous nous intéressons au comportement en temps long et quand le nombre de particules devient grand, d'un système de processus de naissance et mort pour lequel les particules interagissent à chaque instant par le biais de la moyenne de leurs positions. Nous établissons l'existence d'une limite macroscopique, solution d'une équation non linéaire ainsi que le phénomène de propagation du chaos avec une estimation quantitative et uniforme en temps
In this thesis we study the asymptotic behavior of particle systems in mean field type interaction in discrete space, where the system acts over one fixed particle through the empirical measure of the system. In the first part of this thesis, we are interested in Fleming-Viot particle systems: the particles move independently of each other until one of them reaches an absorbing state. At this time, the absorbed particle jumps instantly to the position of one of the other particles, chosen uniformly at random. The ergodicity of the process is established in the case of random walks on N with a dirft towards the origin and on complete graph dynamics. For the latter, we obtain a quantitative estimate of the convergence described by the Wasserstein curvature. Moreover, under the invariant measure, we show the convergence of the empirical measure towards the unique quasi-stationary distribution as the size of the system tends to infinity. In the second part of this thesis, we study the behavior in large time and when the number of particles is large of a system of birth and death processes where at each time a particle interacts with the others through the mean of theirs positions. We establish the existence of a macroscopic limit, solution of a non linear equation and the propagation of chaos phenomenon with quantitative and uniform in time estimate
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Loubaton, Philippe. "Prediction et representation markovienne des processus stationnaires vectoriels sur z::(2) : utilisation de techniques d'estimation spectrale 2-d en traitement d'antenne." Paris, ENST, 1988. http://www.theses.fr/1988ENST0012.

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Books on the topic "Stationary random process"

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Yaglom, A. M. Correlation theory of stationary and related random functions. New York: Springer-Verlag, 1987.

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Yaglom, A. M. Correlation theory of stationary and related random functions. New York: Springer-Verlag, 1987.

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Correlation theory of stationary and related random functions. New York: Springer-Verlag, 1987.

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Marcus, Michael B. [xé]-radial processes and random Fourier series. Providence, R.I., USA: American Mathematical Society, 1987.

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An introduction to the theory of stationary random functions. Mineola, N.Y: Dover Publications, 2004.

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Coolen, A. C. C., A. Annibale, and E. S. Roberts. Random graph ensembles. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0003.

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This chapter presents some theoretical tools for defining random graph ensembles systematically via soft or hard topological constraints including working through some properties of the Erdös-Rényi random graph ensemble, which is the simplest non-trivial random graph ensemble where links appear between two nodes with a fixed probability p. The chapter sets out the central representation of graph generation as the result of a discrete-time Markovian stochastic process. This unites the two flavours of graph generation approaches – because they can be viewed as simply moving forwards or backwards through this representation. It is possible to define a random graph by an algorithm, and then calculate the associated stationary probability. The alternative approach is to specify sampling weights and then to construct an algorithm that will have these weights as the stationary probabilities upon convergence.
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Stationary Random Processes Associated with Point Processes. Springer, 2012.

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Coolen, A. C. C., A. Annibale, and E. S. Roberts. Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0001.

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This introductory chapter sets the scene for the material which follows by briefly introducing the study of networks and describing their wide scope of application. It discusses the role of well-specified random graphs in setting network science onto a firm scientific footing, emphasizing the importance of well-defined null models. Non-trivial aspects of graph generation are introduced. An important distinction is made between approaches that begin with a desired probability distribution on the final graph ensembles and approaches where the graph generation process is the main object of interest and the challenge is to analyze the expected topological properties of the generated networks. At the core of the graph generation process is the need to establish a mathematical connection between the stochastic graph generation process and the stationary probability distribution to which these processes evolve.
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Boudou, Alain, and Yves Romain. On Product Measures Associated with Stationary Processes. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.15.

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This article considers the connections between product measures and stationary processes. It first provides an overview of historical facts and relevant terminology, basic concepts and the mathematical approach. In particular, it discusses random measures, the projection-valued spectral measure (PVSM), convolution products, and the association between shift operators and PVSMs. It then presents the main results and their first potential applications, focusing on stochastic integrals, the image of a random measure under measurable mapping, the existence of a transport-type theorem, and the transpose of a continuous homomorphism between groups. It also describes the PVSM associated with a unitary operator, the convolution product of two PVSMs, the unitary operators generated by a PVSM, extension of the convolution product of two PVSMs, an equation where the unknown quantity is a PVSM, and the convolution product of two random measures. The article concludes with an analysis of mathematical developments related to the previous results.
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Thorisson, Hermann. Coupling, Stationarity, and Regeneration (Probability and its Applications). Springer, 2000.

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Book chapters on the topic "Stationary random process"

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Zorine, Andrei V., and Kseniya O. Sizova. "A Method for Solving Stationary Equations for Priority Time-Sharing Service Process in Random Environment." In Information Technologies and Mathematical Modelling. Queueing Theory and Applications, 304–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72247-0_23.

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Rozanov, Yuriĭ A. "Stationary Processes." In Introduction to Random Processes, 84–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-72717-7_13.

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Mauro, Raffaele. "Traffic Flow Stationarity." In Traffic and Random Processes, 55–62. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09324-6_4.

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Koralov, Leonid, and Yakov G. Sinai. "Strictly Stationary Random Processes." In Theory of Probability and Random Processes, 231–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-540-68829-7_16.

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Koralov, Leonid, and Yakov G. Sinai. "Wide-Sense Stationary Random Processes." In Theory of Probability and Random Processes, 209–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-540-68829-7_15.

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Kay, Steven M. "Wide Sense Stationary Random Processes." In Intuitive Probability and Random Processes Using MATLAB®, 547–96. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/0-387-24158-2_17.

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Tse-Pei, Chiang. "Multiplicity Properties of Stationary Second Order Random Fields." In Stochastic Processes, 31–40. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4615-7909-0_5.

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Kay, Steven M. "Multiple Wide Sense Stationary Random Processes." In Intuitive Probability and Random Processes Using MATLAB®, 641–71. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/0-387-24158-2_19.

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Leadbetter, M. R., and Holger Rootzén. "On Extreme Values in Stationary Random Fields." In Stochastic Processes and Related Topics, 275–85. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-2030-5_15.

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Kay, Steven M. "Linear Systems and Wide Sense Stationary Random Processes." In Intuitive Probability and Random Processes Using MATLAB®, 597–639. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/0-387-24158-2_18.

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Conference papers on the topic "Stationary random process"

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Benhenni, Karim, and Mustapha Rachdi. "Bispectrum estimation for a continuous-time stationary process from a random sampling." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0053.

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Chernoyarov, Oleg V., Mahdi M. Shahmoradian, Maksim I. Maksimov, and Alexandra V. Salnikova. "The estimate of the dispersion of the fast-fluctuating stationary Gaussian random process." In 2017 3rd International Conference on Frontiers of Signal Processing (ICFSP). IEEE, 2017. http://dx.doi.org/10.1109/icfsp.2017.8097054.

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Muramatsu, Toshiharu. "Numerical Investigations of a Turbulence Mixing Process Related to Thermal Striping Phenomena at a T-Junction of Liquid Metal Fast Reactor Piping Systems." In ASME 2002 Pressure Vessels and Piping Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/pvp2002-1572.

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Fluid-structure thermal interaction phenomena characterized by stationary random temperature fluctuations, namely thermal striping are observed in the downstream region of a T-junction piping system of liquid metal fast reactor (LMFR). Therefore the piping walls located in the downstream region must be protected against the stationary random thermal process which might induced high-cycle fatigue. This paper describes the evaluation system based on numerical simulation methods for the thermal striping, and numerical results of the thermal striping at a T-junction piping system under the various parameters, i.e., velocity ratio and diameter ratio between both the pipes and Reynolds number. Then detailed turbulence mixing process at the T-junction piping region due to arched vortexes generating lower frequency fluctuations are evaluated through a separate numerical analysis of a fundamental water experiment.
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4

Degrange, Bernard. "The emission of blazars in VHE gamma-rays viewed as a random stationary process: the case of PKS 2155-304." In Workshop on Blazar Variability across the Electromagnetic Spectrum. Trieste, Italy: Sissa Medialab, 2009. http://dx.doi.org/10.22323/1.063.0016.

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5

Khalil, Mohamed, Roland Wüchner, and Kai-Uwe Bletzinger. "Generalization of Spectral Methods for High-Cycle Fatigue Analysis to Accommodate Non-Stationary Random Processes." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9074.

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Abstract Estimation of material fatigue life is an essential task in many engineering fields. When non-proportional loads are applied, the methodology to estimate fatigue life grows in complexity. Many methods have been proposed to solve this problem both in time and frequency domains. The former tends to give more accurate results, while the latter seems to be more computationally favorable. Until now, the focus of frequency-based methods has been limited to signals assumed to follow a stationary statistic process. This work proposes a generalization to the existing methods to accommodate non-stationary processes as well. A sensitivity analysis is conducted on the influence of the formulation’s hyper-parameters, followed by a numerical investigation on different signals and various materials to assert the robustness of the method.
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Xu, Yunfei, and Jongeun Choi. "Spatial Prediction With Mobile Sensor Networks Using Gaussian Process Regression Based on Gaussian Markov Random Fields." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6092.

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In this paper, a new class of Gaussian processes is proposed for resource-constrained mobile sensor networks. Such a Gaussian process builds on a GMRF with respect to a proximity graph over a surveillance region. The main advantages of using this class of Gaussian processes over standard Gaussian processes defined by mean and covariance functions are its numerical efficiency and scalability due to its built-in GMRF and its capability of representing a wide range of non-stationary physical processes. The formulas for Bayesian posterior predictive statistics such as prediction mean and variance are derived and a sequential field prediction algorithm is provided for sequentially sampled observations. For a special case using compactly supported kernels, we propose a distributed algorithm to implement field prediction by correctly fusing all observations in Bayesian statistics. Simulation results illustrate the effectiveness of our approach.
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Baldwin, J. D., J. G. Thacker, and T. T. Baber. "Estimation of Structural Reliability Under Random Fatigue Conditions." In ASME 1991 Design Technical Conferences. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/detc1991-0007.

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Abstract A procedure for determining the reliability of a structural element or machine part is presented. The solution assumes that the stress history in the part can be described by a narrow band, stationary, Gaussian random process and that the fatigue behavior of the part is governed by the classical S-N curve. Endurance limit modifications are discussed and a procedure is presented for determining the mean and standard deviation of the static stresses acting throughout a structure. A numerical example is presented showing the method applied to estimate the fatigue reliability of a power wheelchair frame.
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Myrhaug, Dag, and Muk Chen Ong. "Random Wave-Induced Burial and Scour of Short Cylinders and Truncated Cones on Mild Slopes." In ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/omae2017-62476.

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A stochastic approach calculating the random wave-induced burial and scour depth of short cylinders and truncated cones on mild slopes is provided. It assumes the waves to be a stationary narrow-band random process and a wave height distribution for mild slopes is adopted, also using formulae for the burial and scour depths for regular waves on horizontal beds for short cylinders and for truncated cones. Examples of results are also provided.
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Huang, Qian, Fenggang Zang, and Yixiong Zhang. "Random Seismic Response Analysis of Coupling Structure Interconnected by Hysteretic Dampers." In 18th International Conference on Nuclear Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/icone18-29140.

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Coupling structure interconnected by hysteretic dampers appears to be an effective method to mitigate structural seismic response. In the paper, the random seismic response is evaluated through the pseudo-excitation principle incorporated with stochastic equivalent linearization method without by solving the Lyapunov differential equation. For which, the seismic excitation is limited to be shot noise process and the computation burden should not be neglected while structural freedoms are large. In the paper, it is supposed that the structures keep elastic all the time and the hysteretic dampers are represented with versatile Bouc-Wen model. With the participation of assistant augment and reduced matrices which are correlated with the location of hysteretic dampers, the unidirectional excitation of one component and spatial excitation of multiple components are derived and the relationship between the pseudo-excitation and pseudo-response is deduced. Then, a pseudo-excitation closed-form expression for the system random response is established. Consequently, the stationary random seismic response of two shear type structure interconnected with hysteretic dampers is analyzed. The structural stationary seismic responses for two methods agree well. parametric studies for the hysteretic dampers and the optimum way to install the hysteretic dampers are also discussed.
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Zhang, Yanqiu, Zhimin Tan, Yucheng Hou, and Jiabei Yuan. "A Study for Statistical Characteristics of Riser Response in Global Dynamic Analysis With Irregular Wave." In ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/omae2014-23196.

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A study was conducted to have a deeper understanding to the statistical characteristics of response of flexible riser in global dynamic simulation with irregular wave. If consider the numerical simulation model as a system and the input wave train as an excitation to it, the time histories of riser load should be the response of the system to the excitation. In order to look the effect of riser configuration and water depth, the study was conducted with three kinds of configuration: Free-Hanging, Lazy-S and Tethered-Wave, which were in different water depths. In order to examine the stationarity and ergodicity of riser response, 100 simulations were performed. Each simulation was performed with a 3-hours-long storm. Except the seeds used to generate the random phases to the wave components, the 100 irregular wave processes for each riser are completely the same. When the number of wave components is enough large, the input irregular wave train should be a stationary normal process. Since the software used for the dynamic simulation is high nonlinear, however, the time history of riser response may not be perfectly stationary normal process. Then different probability distribution theories were applied to fit these time histories and the most fitting one was found out for different riser responses and for different riser configurations. The ensemble autocorrelation functions and the time autocorrelation functions were also examined for both irregular waves and the riser responses. Then the study indicated that both irregular waves and riser responses as random processes should be ergodic stationary. Finally the cross correlations between the irregular waves and riser responses were also examined and it was found that the irregular wave for each riser should be jointly stationary with each response of the riser.
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Reports on the topic "Stationary random process"

1

Leadbetter, M. R. On the Exeedance Random Measures for Stationary Processes. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada192838.

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2

Miamee, A. G. On Determining the Predictor of Non-Full-Rank Multivariate Stationary Random Processes. Fort Belvoir, VA: Defense Technical Information Center, March 1985. http://dx.doi.org/10.21236/ada159165.

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You might also be interested in the extended bibliographies on the topic 'Stationary random process' for particular source types:
Journal articles
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