Relevant bibliographies by topics / Long-range dependence

Academic literature on the topic 'Long-range dependence'

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Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Long-range dependence"

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Samorodnitsky, Gennady. "Long Range Dependence." Foundations and Trends® in Stochastic Systems 1, no. 3 (2006): 163–257. http://dx.doi.org/10.1561/0900000004.

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Beran, Jan. "Long‐range dependence." WIREs Computational Statistics 2, no. 1 (January 2010): 26–35. http://dx.doi.org/10.1002/wics.52.

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Joshi, Prashant. "Analyzing Long Range Dependence in Stock Markets of India." Indian Journal of Applied Research 4, no. 8 (October 1, 2011): 345–48. http://dx.doi.org/10.15373/2249555x/august2014/87.

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Dempster, Arthur P., and Jing-Shiang Hwang. "[Statistical Methods for Data with Long-Range Dependence]: Comment: Short- Range Consequences of Long-Range Dependence." Statistical Science 7, no. 4 (November 1992): 416–20. http://dx.doi.org/10.1214/ss/1177011123.

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Heyde, C. C., and Y. Yang. "On defining long-range dependence." Journal of Applied Probability 34, no. 4 (December 1997): 939–44. http://dx.doi.org/10.2307/3215008.

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Long-range dependence has usually been defined in terms of covariance properties relevant only to second-order stationary processes. Here we provide new definitions, almost equivalent to the original ones in that domain of applicability, which are useful for processes which may not be second-order stationary, or indeed have infinite variances. The ready applicability of this formulation for categorizing the behaviour for various infinite variance models is shown.
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Heyde, C. C., and Y. Yang. "On defining long-range dependence." Journal of Applied Probability 34, no. 04 (December 1997): 939–44. http://dx.doi.org/10.1017/s0021900200101639.

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Long-range dependence has usually been defined in terms of covariance properties relevant only to second-order stationary processes. Here we provide new definitions, almost equivalent to the original ones in that domain of applicability, which are useful for processes which may not be second-order stationary, or indeed have infinite variances. The ready applicability of this formulation for categorizing the behaviour for various infinite variance models is shown.
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Surgailis, Donatas. "Long-range dependence and Appell rank." Annals of Probability 28, no. 1 (January 2000): 478–97. http://dx.doi.org/10.1214/aop/1019160127.

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Ma, Chunsheng. "Correlation models with long-range dependence." Journal of Applied Probability 39, no. 2 (June 2002): 370–82. http://dx.doi.org/10.1239/jap/1025131432.

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This paper is concerned with the correlation structure of a stationary discrete time-series with long memory or long-range dependence. Given a sequence of bounded variation, we obtain necessary and sufficient conditions for a function generated from the sequence to be a proper correlation function. These conditions are applied to derive various slowly decaying correlation models. To obtain correlation models with short-range dependence from an absolutely summable sequence, a simple method is introduced.
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Heyde, C. C. "On modes of long-range dependence." Journal of Applied Probability 39, no. 4 (December 2002): 882–88. http://dx.doi.org/10.1239/jap/1037816026.

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This paper aims at enhancing the understanding of long-range dependence (LRD) by focusing on mechanisms for generating this dependence, namely persistence of signs and/or persistence of magnitudes beyond what can be expected under weak dependence. These concepts are illustrated through a discussion of fractional Brownian noise of index H ∈ (0,1) and it is shown that LRD in signs holds if and only if ½ < H < 1 and LRD in magnitudes if and only if ¾ ≤ H < 1. An application to discrimination between two risky asset finance models, the FATGBM model of Heyde and the multifractal model of Mandelbrot, is given to illustrate the use of the ideas.
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Csorgo, Sandor, and Jan Mielniczuk. "Density Estimation Under Long-Range Dependence." Annals of Statistics 23, no. 3 (June 1995): 990–99. http://dx.doi.org/10.1214/aos/1176324632.

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Dissertations / Theses on the topic "Long-range dependence"

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Vivero, Oskar. "Estimation of long-range dependence." Thesis, University of Manchester, 2010. https://www.research.manchester.ac.uk/portal/en/theses/estimation-of-longrange-dependence(65565876-4ec6-44b3-8181-51b13dca309c).html.

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A set of observations from a random process which exhibit correlations that decay slower than an exponential rate is regarded as long-range dependent. This phenomenon has stimulated great interest in the scientific community as it appears in a wide range of areas of knowledge. For example, this property has been observed in data pertaining to electronics, econometrics, hydrology and biomedical signals.There exist several estimation methods for finding model parameters that help explain the set of observations exhibiting long-range dependence. Among these methods, maximum likelihood is attractive, given its desirable statistical properties such as asymptotic consistency and efficiency. However, its computational complexity makes the implementation of maximum likelihood prohibitive.This thesis presents a group of computationally efficient estimators based on the maximum likelihood framework. The thesis consists of two main parts. The first part is devoted to developing a computationally efficient alternative to the maximum likelihood estimate. This alternative is based on the circulant embedding concept and it is shown to maintain the desirable statistical properties of maximum likelihood.Interesting results are obtained by analysing the circulant embedding estimate. In particular, this thesis shows that the maximum likelihood based methods are ill-conditioned; the estimators' performance will deteriorate significantly when the set of observations is corrupted by errors. The second part of this thesis focuses on developing computationally efficient estimators with improved performance under the presence of errors in the observations.
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Carpio, Kristine Joy Espiritu, and kjecarpio@lycos com. "Long-Range Dependence of Markov Processes." The Australian National University. School of Mathematical Sciences, 2006. http://thesis.anu.edu.au./public/adt-ANU20061024.131933.

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Long-range dependence in discrete and continuous time Markov chains over a countable state space is defined via embedded renewal processes brought about by visits to a fixed state. In the discrete time chain, solidarity properties are obtained and long-range dependence of functionals are examined. On the other hand, the study of LRD of continuous time chains is defined via the number of visits in a given time interval. Long-range dependence of Markov chains over a non-countable state space is also carried out through positive Harris chains. Embedded renewal processes in these chains exist via visits to sets of states called proper atoms. Examples of these chains are presented, with particular attention given to long-range dependent Markov chains in single-server queues, namely, the waiting times of GI/G/1 queues and queue lengths at departure epochs in M/G/1 queues. The presence of long-range dependence in these processes is dependent on the moment index of the lifetime distribution of the service times. The Hurst indexes are obtained under certain conditions on the distribution function of the service times and the structure of the correlations. These processes of waiting times and queue sizes are also examined in a range of M/P/2 queues via simulation (here, P denotes a Pareto distribution).
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Carpio, Kristine Joy Espiritu. "Long-range dependence of Markov processes /." View thesis entry in Australian Digital Theses Program, 2006. http://thesis.anu.edu.au/public/adt-ANU20061024.131933/index.html.

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Gaigalas, Raimundas. "A Non-Gaussian Limit Process with Long-Range Dependence." Doctoral thesis, Uppsala : Matematiska institutionen, Univ. [distributör], 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3993.

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Trovero, Michele A. Smith Richard L. "Effects of aggregation on estimators of long-range dependence." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2007. http://dc.lib.unc.edu/u?/etd,1170.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2007.
Title from electronic title page (viewed Mar. 27, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Statistics and Operations Research." Discipline: Statistics and Operations Research; Department/School: Statistics and Operations Research.
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Finlay, Richard. "The Variance Gamma (VG) Model with Long Range Dependence." Thesis, The University of Sydney, 2009. http://hdl.handle.net/2123/5434.

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This thesis mainly builds on the Variance Gamma (VG) model for financial assets over time of Madan & Seneta (1990) and Madan, Carr & Chang (1998), although the model based on the t distribution championed in Heyde & Leonenko (2005) is also given attention. The primary contribution of the thesis is the development of VG models, and the extension of t models, which accommodate a dependence structure in asset price returns. In particular it has become increasingly clear that while returns (log price increments) of historical financial asset time series appear as a reasonable approximation of independent and identically distributed data, squared and absolute returns do not. In fact squared and absolute returns show evidence of being long range dependent through time, with autocorrelation functions that are still significant after 50 to 100 lags. Given this evidence against the assumption of independent returns, it is important that models for financial assets be able to accommodate a dependence structure.
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Finlay, Richard. "The Variance Gamma (VG) Model with Long Range Dependence." University of Sydney, 2009. http://hdl.handle.net/2123/5434.

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Doctor of Philosophy (PhD)
This thesis mainly builds on the Variance Gamma (VG) model for financial assets over time of Madan & Seneta (1990) and Madan, Carr & Chang (1998), although the model based on the t distribution championed in Heyde & Leonenko (2005) is also given attention. The primary contribution of the thesis is the development of VG models, and the extension of t models, which accommodate a dependence structure in asset price returns. In particular it has become increasingly clear that while returns (log price increments) of historical financial asset time series appear as a reasonable approximation of independent and identically distributed data, squared and absolute returns do not. In fact squared and absolute returns show evidence of being long range dependent through time, with autocorrelation functions that are still significant after 50 to 100 lags. Given this evidence against the assumption of independent returns, it is important that models for financial assets be able to accommodate a dependence structure.
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Rust, Henning. "Detection of long-range dependence : applications in climatology and hydrology." Phd thesis, Universität Potsdam, 2007. http://opus.kobv.de/ubp/volltexte/2007/1334/.

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It is desirable to reduce the potential threats that result from the variability of nature, such as droughts or heat waves that lead to food shortage, or the other extreme, floods that lead to severe damage. To prevent such catastrophic events, it is necessary to understand, and to be capable of characterising, nature's variability. Typically one aims to describe the underlying dynamics of geophysical records with differential equations. There are, however, situations where this does not support the objectives, or is not feasible, e.g., when little is known about the system, or it is too complex for the model parameters to be identified. In such situations it is beneficial to regard certain influences as random, and describe them with stochastic processes. In this thesis I focus on such a description with linear stochastic processes of the FARIMA type and concentrate on the detection of long-range dependence. Long-range dependent processes show an algebraic (i.e. slow) decay of the autocorrelation function. Detection of the latter is important with respect to, e.g. trend tests and uncertainty analysis. Aiming to provide a reliable and powerful strategy for the detection of long-range dependence, I suggest a way of addressing the problem which is somewhat different from standard approaches. Commonly used methods are based either on investigating the asymptotic behaviour (e.g., log-periodogram regression), or on finding a suitable potentially long-range dependent model (e.g., FARIMA[p,d,q]) and test the fractional difference parameter d for compatibility with zero. Here, I suggest to rephrase the problem as a model selection task, i.e.comparing the most suitable long-range dependent and the most suitable short-range dependent model. Approaching the task this way requires a) a suitable class of long-range and short-range dependent models along with suitable means for parameter estimation and b) a reliable model selection strategy, capable of discriminating also non-nested models. With the flexible FARIMA model class together with the Whittle estimator the first requirement is fulfilled. Standard model selection strategies, e.g., the likelihood-ratio test, is for a comparison of non-nested models frequently not powerful enough. Thus, I suggest to extend this strategy with a simulation based model selection approach suitable for such a direct comparison. The approach follows the procedure of a statistical test, with the likelihood-ratio as the test statistic. Its distribution is obtained via simulations using the two models under consideration. For two simple models and different parameter values, I investigate the reliability of p-value and power estimates obtained from the simulated distributions. The result turned out to be dependent on the model parameters. However, in many cases the estimates allow an adequate model selection to be established. An important feature of this approach is that it immediately reveals the ability or inability to discriminate between the two models under consideration. Two applications, a trend detection problem in temperature records and an uncertainty analysis for flood return level estimation, accentuate the importance of having reliable methods at hand for the detection of long-range dependence. In the case of trend detection, falsely concluding long-range dependence implies an underestimation of a trend and possibly leads to a delay of measures needed to take in order to counteract the trend. Ignoring long-range dependence, although present, leads to an underestimation of confidence intervals and thus to an unjustified belief in safety, as it is the case for the return level uncertainty analysis. A reliable detection of long-range dependence is thus highly relevant in practical applications. Examples related to extreme value analysis are not limited to hydrological applications. The increased uncertainty of return level estimates is a potentially problem for all records from autocorrelated processes, an interesting examples in this respect is the assessment of the maximum strength of wind gusts, which is important for designing wind turbines. The detection of long-range dependence is also a relevant problem in the exploration of financial market volatility. With rephrasing the detection problem as a model selection task and suggesting refined methods for model comparison, this thesis contributes to the discussion on and development of methods for the detection of long-range dependence.
Die potentiellen Gefahren und Auswirkungen der natürlicher Klimavariabilitäten zu reduzieren ist ein wünschenswertes Ziel. Solche Gefahren sind etwa Dürren und Hitzewellen, die zu Wasserknappheit führen oder, das andere Extrem, Überflutungen, die einen erheblichen Schaden an der Infrastruktur nach sich ziehen können. Um solche katastrophalen Ereignisse zu vermeiden, ist es notwendig die Dynamik der Natur zu verstehen und beschreiben zu können. Typischerweise wird versucht die Dynamik geophysikalischer Datenreihen mit Differentialgleichungssystemen zu beschreiben. Es gibt allerdings Situationen in denen dieses Vorgehen nicht zielführend oder technisch nicht möglich ist. Dieses sind Situationen in denen wenig Wissen über das System vorliegt oder es zu komplex ist um die Modellparameter zu identifizieren. Hier ist es sinnvoll einige Einflüsse als zufällig zu betrachten und mit Hilfe stochastischer Prozesse zu modellieren. In dieser Arbeit wird eine solche Beschreibung mit linearen stochastischen Prozessen der FARIMA-Klasse angestrebt. Besonderer Fokus liegt auf der Detektion von langreichweitigen Korrelationen. Langreichweitig korrelierte Prozesse sind solche mit einer algebraisch, d.h. langsam, abfallenden Autokorrelationsfunktion. Eine verläßliche Erkennung dieser Prozesse ist relevant für Trenddetektion und Unsicherheitsanalysen. Um eine verläßliche Strategie für die Detektion langreichweitig korrelierter Prozesse zur Verfügung zu stellen, wird in der Arbeit ein anderer als der Standardweg vorgeschlagen. Gewöhnlich werden Methoden eingesetzt, die das asymptotische Verhalten untersuchen, z.B. Regression im Periodogramm. Oder aber es wird versucht ein passendes potentiell langreichweitig korreliertes Modell zu finden, z.B. aus der FARIMA Klasse, und den geschätzten fraktionalen Differenzierungsparameter d auf Verträglichkeit mit dem trivialen Wert Null zu testen. In der Arbeit wird vorgeschlagen das Problem der Detektion langreichweitiger Korrelationen als Modellselektionsproblem umzuformulieren, d.h. das beste kurzreichweitig und das beste langreichweitig korrelierte Modell zu vergleichen. Diese Herangehensweise erfordert a) eine geeignete Klasse von lang- und kurzreichweitig korrelierten Prozessen und b) eine verläßliche Modellselektionsstrategie, auch für nichtgenestete Modelle. Mit der flexiblen FARIMA-Klasse und dem Whittleschen Ansatz zur Parameterschätzung ist die erste Voraussetzung erfüllt. Hingegen sind standard Ansätze zur Modellselektion, wie z.B. der Likelihood-Ratio-Test, für nichtgenestete Modelle oft nicht trennscharf genug. Es wird daher vorgeschlagen diese Strategie mit einem simulationsbasierten Ansatz zu ergänzen, der insbesondere für die direkte Diskriminierung nichtgenesteter Modelle geeignet ist. Der Ansatz folgt einem statistischen Test mit dem Quotienten der Likelihood als Teststatistik. Ihre Verteilung wird über Simulationen mit den beiden zu unterscheidenden Modellen ermittelt. Für zwei einfache Modelle und verschiedene Parameterwerte wird die Verläßlichkeit der Schätzungen für p-Wert und Power untersucht. Das Ergebnis hängt von den Modellparametern ab. Es konnte jedoch in vielen Fällen eine adäquate Modellselektion etabliert werden. Ein wichtige Eigenschaft dieser Strategie ist, dass unmittelbar offengelegt wird, wie gut sich die betrachteten Modelle unterscheiden lassen. Zwei Anwendungen, die Trenddetektion in Temperaturzeitreihen und die Unsicherheitsanalyse für Bemessungshochwasser, betonen den Bedarf an verläßlichen Methoden für die Detektion langreichweitiger Korrelationen. Im Falle der Trenddetektion führt ein fälschlicherweise gezogener Schluß auf langreichweitige Korrelationen zu einer Unterschätzung eines Trends, was wiederum zu einer möglicherweise verzögerten Einleitung von Maßnahmen führt, die diesem entgegenwirken sollen. Im Fall von Abflußzeitreihen führt die Nichtbeachtung von vorliegenden langreichweitigen Korrelationen zu einer Unterschätzung der Unsicherheit von Bemessungsgrößen. Eine verläßliche Detektion von langreichweitig Korrelierten Prozesse ist somit von hoher Bedeutung in der praktischen Zeitreihenanalyse. Beispiele mit Bezug zu extremem Ereignissen beschränken sich nicht nur auf die Hochwasseranalyse. Eine erhöhte Unsicherheit in der Bestimmung von extremen Ereignissen ist ein potentielles Problem von allen autokorrelierten Prozessen. Ein weiteres interessantes Beispiel ist hier die Abschätzung von maximalen Windstärken in Böen, welche bei der Konstruktion von Windrädern eine Rolle spielt. Mit der Umformulierung des Detektionsproblems als Modellselektionsfrage und mit der Bereitstellung geeigneter Modellselektionsstrategie trägt diese Arbeit zur Diskussion und Entwicklung von Methoden im Bereich der Detektion von langreichweitigen Korrelationen bei.
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Pilipauskaité, Vytauté. "Limit theorems for spatio-temporal models with long-range dependence." Thesis, Nantes, 2017. http://www.theses.fr/2017NANT4057/document.

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Les travaux de la thèse portent sur les théorèmes limites pour des modèles stochastiques à forte dépendance. Dans la première partie, nous considérons des modèles AR(1) à coefficient aléatoire. Nous identifions trois régimes asymptotiques différents pour le schéma d’agrégation conjointe temporelle-contemporaine lorsque les processus AR sont indépendants et lorsque les AR possède des innovations communes. Ensuite, on discute de l’estimation non paramétrique de la fonction de répartition du coefficient autorégressif à partir d’un panel de séries AR(1) à coefficient aléatoire. Nous prouvons la convergence faible du processus empirique basé sur des estimations des coefficients autorégressifs non observables vers un pont brownien généralisé. Ce résultat est ensuite appliqué pour valider différents outils d’inférence statistique à partir des données du panel AR(1). Dans la deuxième partie de la thèse, nous nous concentrons sur les modèles spatiaux en dimension 2. Nous considérons des champs aléatoires construits à partir des polynômes Appell et de champs aléatoires linéaires. Pour ce modèle non linéaire, nous étudions la limite de ses sommes partielles normalisées prises sur des rectangles et prouvons l’existence d’une transition d’échelle. Enfin, nous abordons la même question pour le modèle de germes-grains aléatoire. Nous mettons en évidence l’existence de deux points de transition dans les limites de ces modèles
The thesis is devoted to limit theorems for stochastic models with long-range dependence. We first consider a random-coefficient AR(1) process, which can have long memory provided the distribution of autoregressive coefficient concentrates near the unit root. We identify three different limit regimes in the scheme of joint temporal-contemporaneous aggregation for independent copies of random-coefficient AR(1) process and for its copies driven by common innovations. Next, we discuss nonparametric estimation of the distribution of the autoregressive coefficient given multiple random-coefficient AR(1) series. We prove the weak convergence of the empirical process based on estimates of unobservable autoregressive coefficients to a generalized Brownian bridge and apply this result to draw statistical inference from panel AR(1) data. In the second part of the thesis we focus on spatial models in dimension 2. We define a nonlinear random field as the Appell polynomial of a linear random field with long-range dependence. For the nonlinear random field, we investigate the limit of its normalized partial sums over rectangles and prove the existence of scaling transition. Finally, we study such like scaling of the random grain model and obtain two-change points in its limits
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Casas, Villalba Isabel. "Statistical inference in continuous-time models with short-range and/or long-range dependence." University of Western Australia. School of Mathematics and Statistics, 2006. http://theses.library.uwa.edu.au/adt-WU2006.0133.

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The aim of this thesis is to estimate the volatility function of continuoustime stochastic models. The estimation of the volatility of the following wellknown international stock market indexes is presented as an application: Dow Jones Industrial Average, Standard and Poor’s 500, NIKKEI 225, CAC 40, DAX 30, FTSE 100 and IBEX 35. This estimation is studied from two different perspectives: a) assuming that the volatility of the stock market indexes displays shortrange dependence (SRD), and b) extending the previous model for processes with longrange dependence (LRD), intermediaterange dependence (IRD) or SRD. Under the efficient market hypothesis (EMH), the compatibility of the Vasicek, the CIR, the Anh and Gao, and the CKLS models with the stock market indexes is being tested. Nonparametric techniques are presented to test the affinity of these parametric volatility functions with the volatility observed from the data. Under the assumption of possible statistical patterns in the volatility process, a new estimation procedure based on the Whittle estimation is proposed. This procedure is theoretically and empirically proven. In addition, its application to the stock market indexes provides interesting results.
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Books on the topic "Long-range dependence"

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Samorodnitsky, Gennady. Long range dependence. Hanover, Mass: Now Publishers, Inc., 2007.

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Samorodnitsky, Gennady. Stochastic Processes and Long Range Dependence. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45575-4.

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Robinson, P. M. Time series regression with long range dependence. London: Suntory and Toyota International Centres for Economics and Related Disciplines, 1997.

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Ercan, Ali, M. Levent Kavvas, and Rovshan K. Abbasov. Long-Range Dependence and Sea Level Forecasting. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01505-7.

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Henry, Marc. An investigation of long range dependence in intra-day foreign exchange rate volatility. London: London School of Economics, Financial Markets Group, 1997.

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Taqqu, Murad S., and Vladas Pipiras. Long-Range Dependence and Self-Similarity. Cambridge University Press, 2017.

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Samorodnitsky, Gennady. Stochastic Processes and Long Range Dependence. Springer, 2016.

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and Applications of Long-Range Dependence. Birkhauser, 2003.

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Samorodnitsky, Gennady. Stochastic Processes and Long Range Dependence. Springer, 2018.

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Taqqu, Murad S., and Vladas Pipiras. Long-Range Dependence and Self-Similarity. Cambridge University Press, 2017.

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Book chapters on the topic "Long-range dependence"

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Lahiri, S. N. "Long-Range Dependence." In Resampling Methods for Dependent Data, 241–59. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-1-4757-3803-2_10.

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Doukhan, Paul. "Long-Range Dependence." In Stochastic Models for Time Series, 189–204. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76938-7_10.

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Ganesh, Ayalvadi, Neil O’Connell, and Damon Wischik. "8. Long Range Dependence." In Lecture Notes in Mathematics, 183–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39889-9_8.

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Rosenblatt, Murray. "Short Range and Long Range Dependence." In Asymptotic Laws and Methods in Stochastics, 283–94. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-3076-0_15.

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Heyde, C. C., and Y. Yang. "On Defining Long-Range Dependence." In Selected Works of C.C. Heyde, 426–31. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5823-5_54.

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Samorodnitsky, Gennady. "Introduction to Long-Range Dependence." In Springer Series in Operations Research and Financial Engineering, 175–91. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45575-4_5.

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Ivanov, Plamen Ch. "Long-Range Dependence in Heartbeat Dynamics." In Processes with Long-Range Correlations, 339–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44832-2_19.

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Chen, Yanqing, Mingzhou Ding, and J. A. Scott Kelso. "Long Range Dependence in Human Sensorimotor Coordination." In Processes with Long-Range Correlations, 309–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44832-2_17.

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Ercan, Ali, M. Levent Kavvas, and Rovshan K. Abbasov. "Long-Range Dependence and ARFIMA Models." In Long-Range Dependence and Sea Level Forecasting, 7–10. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01505-7_2.

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Beran, Jan, Britta Steffens, and Sucharita Ghosh. "Long-Range Dependence in Directional Data." In Forum for Interdisciplinary Mathematics, 395–406. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1044-9_21.

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Conference papers on the topic "Long-range dependence"

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Stolojescu, Cristina, Sorin Moga, Philippe Lenca, and Alexandru Isar. "Long-range dependence in WiMAX downlink traffic." In 2011 10th International Symposium on Signals, Circuits and Systems (ISSCS). IEEE, 2011. http://dx.doi.org/10.1109/isscs.2011.5978702.

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Kwon, Han-Seung, Artem Lenskiy, and Kseniya Koneva. "Trading Foreign Currencies Based on Long Range Dependence." In 2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation (CIMSiM). IEEE, 2012. http://dx.doi.org/10.1109/cimsim.2012.73.

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"Long-Range Dependence at the Disk Drive Level." In Third International Conference on the Quantitative Evaluation of Systems - (QEST'06). IEEE, 2006. http://dx.doi.org/10.1109/qest.2006.27.

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Gonzalez, J., G. Patino, M. Strum, and Wang Jiang Chau. "Long range dependence in intrachip transaction level traffic." In 2013 IEEE 4th Latin American Symposium on Circuits and Systems (LASCAS). IEEE, 2013. http://dx.doi.org/10.1109/lascas.2013.6519074.

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Pagano, Michele. "Self-similarity and Long Range Dependence in teletraffic." In 2019 15th International Asian School-Seminar "Optimization Problems of Complex Systems (OPCS). IEEE, 2019. http://dx.doi.org/10.1109/opcs.2019.8880260.

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Roughan, M., and D. Veitch. "Measuring long-range dependence under changing traffic conditions." In IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320). IEEE, 1999. http://dx.doi.org/10.1109/infcom.1999.752173.

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Shalalfeh, L., P. Bogdan, and E. Jonckheere. "Evidence of long-range dependence in power grid." In 2016 IEEE Power and Energy Society General Meeting (PESGM). IEEE, 2016. http://dx.doi.org/10.1109/pesgm.2016.7742029.

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Ilyas, M. U., and H. Radha. "Long Range Dependence of IEEE 802.15.4 Wireless Channels." In 2008 IEEE International Conference on Communications. IEEE, 2008. http://dx.doi.org/10.1109/icc.2008.800.

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"Traffic engineering for VBR video with long-range dependence." In 1996 International IFIP-IEEE Conference on Broadband Communications, Global Infrastructure for the Information Age. IEEE, 1996. http://dx.doi.org/10.1109/icbc.1996.887816.

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Gauss, Roger. "The Dependence of Long-Range Reverberation on Bottom Roughness." In HIGH FREQUENCY OCEAN ACOUSTICS: High Frequency Ocean Acoustics Conference. AIP, 2004. http://dx.doi.org/10.1063/1.1843008.

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Reports on the topic "Long-range dependence"

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Weerasinghe, Ananda P. Stochastic Control Problems for Processes with Long-Range Dependence & Internet Traffic. Fort Belvoir, VA: Defense Technical Information Center, December 2010. http://dx.doi.org/10.21236/ada545731.

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Resnick, S. I., and G. Samorodnitsky. Probabilistic and Statistical Modeling of Complex Systems Exhibiting Long Range Dependence and Heavy Tails. Fort Belvoir, VA: Defense Technical Information Center, July 2010. http://dx.doi.org/10.21236/ada533576.

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Ratilal, Purnima. Enhancing Long Range Sonar Performance in Range-Dependent Fluctuating Ocean Waveguides by Mitigating Biological Clutter and Environmental Reverberation. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada575025.

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Ratilal, Purnima. Enhancing Long Range Sonar Performance in Range-Dependent Fluctuating Ocean Waveguides by Mitigating Biological Clutter and Environmental Reverberation. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada598576.

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Baltagi, Badi H., Georges Bresson, Anoop Chaturvedi, and Guy Lacroix. Robust dynamic space-time panel data models using ε-contamination: An application to crop yields and climate change. CIRANO, January 2023. http://dx.doi.org/10.54932/ufyn4045.

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This paper extends the Baltagi et al. (2018, 2021) static and dynamic ε-contamination papers to dynamic space-time models. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, we consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner (1986)’s g-priors for the variance-covariance matrices. We propose a general “toolbox” for a wide range of specifications which includes the dynamic space-time panel model with random effects, with cross-correlated effects `a la Chamberlain, for the Hausman-Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using an extensive Monte Carlo simulation study, we compare the finite sample properties of our proposed estimator to those of standard classical estimators. We illustrate our robust Bayesian estimator using the same data as in Keane and Neal (2020). We obtain short run as well as long run effects of climate change on corn producers in the United States.
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Patilal, Purnima. Determining the Characteristics and Mechanisms for Biological Clutter and Environmental Reverberation and Their Impact on Long Range Sonar Performance in Range-Dependent Fluctuating Ocean Waveguides. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada571775.

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Grumet, Rebecca, and Benjamin Raccah. Identification of Potyviral Domains Controlling Systemic Infection, Host Range and Aphid Transmission. United States Department of Agriculture, July 2000. http://dx.doi.org/10.32747/2000.7695842.bard.

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Potyviruses form one of the largest and most economically important groups of plant viruses. Individual potyviruses and their isolates vary in symptom expression, host range, and ability to overcome host resistance genes. Understanding factors influencing these biological characteristics is of agricultural importance for epidemiology and deployment of resistance strategies. Cucurbit crops are subject to severe losses by several potyviruses including the highly aggressive and variable zucchini yellow mosaic virus (ZYMV). In this project we sought to investigate protein domains in ZYMV that influence systemic infection and host range. Particular emphasis was on coat protein (CP), because of known functions in both cell to cell and long distance movement, and helper component-protease (HC-Pro), which has been implicated to play a role in symptom development and long distance movement. These two genes are also essential for aphid mediated transmission, and domains that influence disease development may also influence transmissibility. The objectives of the approved BARD project were to test roles of specific domains in the CP and HC-Pro by making sequence alterations or switches between different isolates and viruses, and testing for infectivity, host range, and aphid transmissibility. These objectives were largely achieved as described below. Finally, we also initiated new research to identify host factors interacting with potyviral proteins and demonstrated interaction between the ZYMV RNA dependent RNA polymerase and host poly-(A)-binding protein (Wang et al., in press). The focus of the CP studies (MSU) was to investigate the role of the highly variable amino terminus (NT) in host range determination and systemic infection. Hybrid ZYMV infectious clones were produced by substituting the CP-NT of ZYMV with either the CP-NT from watermelon mosaic virus (overlapping, but broader host range) or tobacco etch virus (TEV) (non- overlapping host range) (Grumet et al., 2000; Ullah ct al., in prep). Although both hybrid viruses initially established systemic infection, indicating that even the non-cucurbit adapted TEV CP-NT could facilitate long distance transport in cucurbits, after approximately 4-6, the plants inoculated with the TEV-CPNT hybrid exhibited a distinct recovery of reduced symptoms, virus titer, and virus specific protection against secondary infection. These results suggest that the plant recognizes the presence of the TEV CP-NT, which has not been adapted to infection of cucurbits, and initiates defense responses. The CP-NT also appears to play a role in naturally occurring resistance conferred by the zym locus in the cucumber line 'Dina-1'. Patterns of virus accumulation indicated that expression of resistance is developmentally controlled and is due to a block in virus movement. Switches between the core and NT domains of ZYMV-NAA (does not cause veinal chlorosis on 'Dina-1'), and ZYMV-Ct (causes veinal chlorosis), indicated that the resistance response likely involves interaction with the CP-NT (Ullah and Grumet, submitted). At the Volcani Center the main thrust was to identify domains in the HC-Pro that affect symptom expression or aphid transmissibility. From the data reported in the first and second year report and in the attached publications (Peng et al. 1998; Kadouri et al. 1998; Raccah et al. 2000: it was shown that: 1. The mutation from PTK to PAK resulted in milder symptoms of the virus on squash, 2. Two mutations, PAK and ATK, resulted in total loss of helper activity, 3. It was established for the first time that the PTK domain is involved in binding of the HC-Pro to the potyvirus particle, and 4. Some of these experiments required greater amount of HC-Pro, therefore a simpler and more efficient purification method was developed based on Ni2+ resin.
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Bowles, David, Michael Williams, Hope Dodd, Lloyd Morrison, Janice Hinsey, Tyler Cribbs, Gareth Rowell, Michael DeBacker, Jennifer Haack-Gaynor, and Jeffrey Williams. Protocol for monitoring aquatic invertebrates of small streams in the Heartland Inventory & Monitoring Network: Version 2.1. National Park Service, April 2021. http://dx.doi.org/10.36967/nrr-2284622.

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The Heartland Inventory and Monitoring Network (HTLN) is a component of the National Park Service’s (NPS) strategy to improve park management through greater reliance on scientific information. The purposes of this program are to design and implement long-term ecological monitoring and provide information for park managers to evaluate the integrity of park ecosystems and better understand ecosystem processes. Concerns over declining surface water quality have led to the development of various monitoring approaches to assess stream water quality. Freshwater streams in network parks are threatened by numerous stressors, most of which originate outside park boundaries. Stream condition and ecosystem health are dependent on processes occurring in the entire watershed as well as riparian and floodplain areas; therefore, they cannot be manipulated independently of this interrelationship. Land use activities—such as timber management, landfills, grazing, confined animal feeding operations, urbanization, stream channelization, removal of riparian vegetation and gravel, and mineral and metals mining—threaten stream quality. Accordingly, the framework for this aquatic monitoring is directed towards maintaining the ecological integrity of the streams in those parks. Invertebrates are an important tool for understanding and detecting changes in ecosystem integrity, and they can be used to reflect cumulative impacts that cannot otherwise be detected through traditional water quality monitoring. The broad diversity of invertebrate species occurring in aquatic systems similarly demonstrates a broad range of responses to different environmental stressors. Benthic invertebrates are sensitive to the wide variety of impacts that influence Ozark streams. Benthic invertebrate community structure can be quantified to reflect stream integrity in several ways, including the absence of pollution sensitive taxa, dominance by a particular taxon combined with low overall taxa richness, or appreciable shifts in community composition relative to reference condition. Furthermore, changes in the diversity and community structure of benthic invertebrates are relatively simple to communicate to resource managers and the public. To assess the natural and anthropo-genic processes influencing invertebrate communities, this protocol has been designed to incorporate the spatial relationship of benthic invertebrates with their local habitat including substrate size and embeddedness, and water quality parameters (temperature, dissolved oxygen, pH, specific conductance, and turbidity). Rigid quality control and quality assurance are used to ensure maximum data integrity. Detailed standard operating procedures (SOPs) and supporting information are associated with this protocol.
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