Relevant bibliographies by topics / Stationarity

Academic literature on the topic 'Stationarity'

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Published: 4 June 2021
Last updated: 10 June 2023

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Journal articles on the topic "Stationarity"

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Yilmaz, A. G., I. Hossain, and B. J. C. Perera. "Effect of climate change and variability on extreme rainfall intensity–frequency–duration relationships: a case study of Melbourne." Hydrology and Earth System Sciences 18, no. 10 (October 15, 2014): 4065–76. http://dx.doi.org/10.5194/hess-18-4065-2014.

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Abstract. The increased frequency and magnitude of extreme rainfall events due to anthropogenic climate change, and decadal and multi-decadal climate variability question the stationary climate assumption. The possible violation of stationarity in climate can cause erroneous estimation of design rainfalls derived from extreme rainfall frequency analysis. This may result in significant consequences for infrastructure and flood protection projects since design rainfalls are essential input for design of these projects. Therefore, there is a need to conduct frequency analysis of extreme rainfall events in the context of non-stationarity, when non-stationarity is present in extreme rainfall events. A methodology consisting of threshold selection, extreme rainfall data (peaks over threshold data) construction, trend and non-stationarity analysis, and stationary and non-stationary generalised Pareto distribution (GPD) models was developed in this paper to investigate trends and non-stationarity in extreme rainfall events, and potential impacts of climate change and variability on intensity–frequency–duration (IFD) relationships. The methodology developed was successfully implemented using rainfall data from an observation station in Melbourne (Australia) for storm durations ranging from 6 min to 72 h. Although statistically significant trends were detected in extreme rainfall data for storm durations of 30 min, 3 h and 48 h, statistical non-stationarity tests and non-stationary GPD models did not indicate non-stationarity for these storm durations and other storm durations. It was also found that the stationary GPD models were capable of fitting extreme rainfall data for all storm durations. Furthermore, the IFD analysis showed that urban flash flood producing hourly rainfall intensities have increased over time.
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Yilmaz, A. G., I. Hossain, and B. J. C. Perera. "Effect of climate change and variability on extreme rainfall intensity–frequency–duration relationships: a case study of Melbourne." Hydrology and Earth System Sciences Discussions 11, no. 6 (June 16, 2014): 6311–42. http://dx.doi.org/10.5194/hessd-11-6311-2014.

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Abstract. The increased frequency and magnitude of extreme rainfall events due to anthropogenic climate change, and decadal and multi-decadal climate variability question the stationary climate assumption. The possible violation of stationarity in climate can cause erroneous estimation of design rainfalls derived from extreme rainfall frequency analysis. This may result in significant consequences for infrastructure and flood protection projects since design rainfalls are essential input for design of these projects. Therefore, there is a need to conduct frequency analysis of extreme rainfall events in the context of non-stationarity, when non-stationarity is present in extreme rainfall events. A methodology consisting of, threshold selection, extreme rainfall data (peaks over threshold data) construction, trend and non-stationarity analysis, and stationary and non-stationary Generalized Pareto Distribution (GPD) models was developed in this paper to investigate trends and non-stationarity in extreme rainfall events, and potential impacts of climate change and variability on Intensity–Frequency–Duration (IFD) relationships. The developed methodology was successfully implemented using rainfall data from an observation station in Melbourne (Australia) for storm durations ranging from 6 min to 72 h. Although statistically significant trends were detected in extreme rainfall data for storm durations of 30 min, and 3 and 48 h, statistical non-stationarity tests and non-stationary GPD models did not indicate non-stationarity for these storm durations and other storm durations. It was also found that the stationary GPD models were capable of fitting extreme rainfall data for all storm durations. Furthermore, the IFD analysis showed that urban flash flood producing hourly rainfall intensities have increased over time.
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Fan, Gai Ling, and Zhi Hua Huang. "Stationarity of the EEG Segment with Event-Related Potentials." Applied Mechanics and Materials 148-149 (December 2011): 30–33. http://dx.doi.org/10.4028/www.scientific.net/amm.148-149.30.

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EEG (electroencephalography), as a noninvasive and inexpensive method, is widely used to measure brain function and make inferences about regional brain activity. The stationarity of EEG has been investigated by many researchers, while the stationarity of EEG segment with ERPs (Event-related Potentials) has hardly been concerned about. It is necessary to analyze the stationarity of this kind of EEG. In this paper, we concentrate on the stationarity of the EEG with ERPs by testing the stationarity of 500ms EEG segments with ERPs recorded from six subjects in two types of experiments. The results suggest that selected EEG segment whose length is larger than 190ms remains to be stationarity and all epochs duration less than 40ms is considered to be stationary, whichever channel the data is from and whatever type of cognitive task is performed in the experiment. This is an obvious difference between the stationarity of EEG with ERPs and that of EEG, which is reported to be stationary as long as its length is less than 12s.
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Shen, Yi, and Tony S. Wirjanto. "Stationarity as a path property." Probability and Mathematical Statistics 39, no. 2 (December 19, 2019): 403–22. http://dx.doi.org/10.19195/0208-4147.39.2.9.

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Traditionally, stationarity refers to shift invariance of the distribution of a stochastic process. In this paper, we rediscover stationarity as a path property instead of a distributional property. More precisely, we characterize a set of paths, denoted by A, which corresponds to the notion of stationarity. On one hand, the set A is shown to be large enough, so that for any stationary process, almost all of its paths are in A. On the other hand, we prove that any path in A will behave in the optimal way under any stationarity test satisfying some mild conditions.
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Valera, Harold Glenn A., Mark J. Holmes, and Gazi M. Hassan. "Does inflation targeting matter for the behavior of inflation and output growth? Some regime-based evidence for Asian economies." Journal of Economic Studies 45, no. 5 (October 8, 2018): 932–55. http://dx.doi.org/10.1108/jes-01-2017-0023.

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PurposeThe purpose of this paper is to consider whether or not the introduction of inflation targeting (IT) impacts on the mean-reversion properties of inflation and output growth.Design/methodology/approachFocusing on eight Asian countries of which four are inflation-targeters, the authors employ a two-state Markov-switching model which characterizes the behavior of inflation and output growth as regime-dependent based on periods of stationarity or non-stationarity.FindingsIn contrast to a literature that offers mixed findings, the authors find the presence of stationary inflation and output growth in one regime for all IT countries, except for South Korea which is characterized by stationary output growth in both regimes. In the cases of South Korea and Thailand, IT reduces the probability of inflation remaining in a non-stationary regime. IT increases the probability of South Korea remaining in a regime of low persistence output growth. While IT is important in understanding behavior, so are other considerations such as exchange rate volatility, as well as the Asian and global financial crises.Originality/valueIn contrast to other unit root tests of inflation and output growth, a novelty of the approach is that the authors obtain new insights in terms of two concepts of stationarity that allow for inflation and output growth to switch between stationary and non-stationary regimes (partial stationarity), or between stationary regimes of differing degrees of persistence (varied stationarity).
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Česnik, Martin, Janko Slavič, Lorenzo Capponi, Massimiliano Palmieri, Filippo Cianetti, and Miha Boltežar. "The relevance of non-stationarities and non-Gaussianities in vibration fatigue." MATEC Web of Conferences 165 (2018): 10011. http://dx.doi.org/10.1051/matecconf/201816510011.

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In classical fatigue of materials, the frequency contents of dynamic loading are well below the natural frequencies of the observed structure or test specimen. However, when dealing with vibration fatigue the frequency contents of dynamic loading and structure's dynamic response overlap, resulting in amplified stress loads of the structure. For such cases, frequency counting methods are especially convenient. Gaussianity and stationarity assumptions are applied in frequency-domain methods for obtaining dynamic structure's response and frequency-domain methods for calculating damage accumulation rate. Since it is common in real environments for the structure to be excited with non-Gaussian and non-stationary loads, this study addresses the effects of such dynamic excitation to experimental time-to-failure of a structure. Initially, the influence of non-Gaussian stationary excitation is experimentally studied via excitation signals with equal power density spectrum and different values of kurtosis. Since no relevant changes of structure's time-to-failure were observed, the study focused on non-stationary excitation signals that are also inherently non-Gaussian. The non-stationarity of excitation was achieved by amplitude modulation and significantly shorter times-to-failure were observed when compared to experiments with stationary non-Gaussian excitation. Additionally, the structure's time-to-failure varied with the rate of the amplitude modulation. To oversee this phenomenon the presented study proposes a non-stationarity index which can be obtained from the excitation time history. The non-stationarity index was experimentally confirmed as a reliable estimator for severity of non-stationary excitation. The non-stationarity index is used to determine if the frequencydomain methods can safely be applied for time-to-failure calculation.
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Lu, Zhiping, Ming Li, and Wei Zhao. "Stationarity Testing of Accumulated Ethernet Traffic." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/217213.

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We investigate the stationarity property of the accumulated Ethernet traffic series. We applied several widely used stationarity and unit root tests, such as Dickey-Fuller test and its augmented version, Phillips-Perron test, as well as the Kwiatkowski-Phillips-Schmidt-Shin test and some of its generalizations, to the assessment of the stationarity of the traffic traces at the different time scales. The quantitative results in this research provide evidence that when the time scale increases, the accumulated traffic series are more stationary.
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Tan, Yi, Jesper Ødum Nielsen, and Gert Frølund Pedersen. "Spatial Stationarity of Ultrawideband and Millimeter Wave Radio Channels." International Journal of Antennas and Propagation 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/3212864.

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For radio channels with broad bandwidth resource, such as those often used for ultrawideband (UWB) and millimeter wave (mmwave) systems, the Wide-Sense Stationary Uncorrelated Scattering (WSSUS) and spatial stationary assumptions are more critical than typical cellular channels with very limited bandwidth resource. This paper studies spatial stationarity and bandwidth dependency of the Multipath Component (MPC) parameters, and the concept of local region of stationarity (LRS) is used as the measure of the physical stationarity region. LRS calculation results based on channel measurements show that the size of LRS is bandwidth dependent in all measured bands, 2–4 GHz, 14–16 GHz, and 28–30 GHz. The results in this paper point out that an inappropriate choice of bandwidth in channel parameter estimation could violate spatial stationary assumptions. The paper indicates LRS sizes for different bandwidths in the three bands.
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Arrieta-Pastrana, Alfonso, Manuel Saba, and Adriana Puello Alcázar. "Analysis of Climate Variability in a Time Series of Precipitation and Temperature Data: A Case Study in Cartagena de Indias, Colombia." Water 14, no. 9 (April 24, 2022): 1378. http://dx.doi.org/10.3390/w14091378.

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Anthropogenic climate change is a global trend, hitherto incontrovertible, causing immense social and economic damage. Although the this is evident at the global level, at the local level, there is still debate about the most appropriate analyses to support this fact. This debate is particularly relevant in developing countries, such as Colombia, where there is a significant lack of data at the local level that require analysis and interpretation. Consequently, studies are often superficially conducted to support climate change theory at the local level. However, such studies are then used to design hydraulic infrastructure, with potential catastrophic errors for human and environmental health. In this study, we sought evidence of climate change through an analysis of a series of data on temperature (maximum, mean and minimum), as well as total annual and maximum rainfall in 24 h registered at the Rafael Nuñez Airport station in the city of Cartagena, Colombia, from 1941 to 2015. The hypotheses of homogeneity, trend, stationarity and non-stationarity were analyzed. Problems of non-homogeneity and the presence of periodicity in the analyzed series were found, showing a trend and apparent non-stationarity in the original series. This could be associated with the effects of climate change. In this case, no correlation was found between temperatures and rainfall. Spectral analysis was performed for all series, and residual series were generated by extracting the harmonics of greatest significance. It was found that the series data generated from the third harmonic are generally stationary and without trend. Therefore, the trend and non-stationarity of the original series are due to problems of non-homogeneity and periodicity in the series. In the results of the stationarity test conducted according to the Phillips–Perron criterion, all series were non-stationary. For the two additional criteria of stationarity tests, 40% were shown to be stationary, and 60% were non-stationary. Specifically, non-homogeneity problems and apparent trends associated with climate change could have negative implications for the design of drainage systems.
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Arrieta-Pastrana, Alfonso, Manuel Saba, and Adriana Puello Alcázar. "Analysis of Climate Variability in a Time Series of Precipitation and Temperature Data: A Case Study in Cartagena de Indias, Colombia." Water 14, no. 9 (April 24, 2022): 1378. http://dx.doi.org/10.3390/w14091378.

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Anthropogenic climate change is a global trend, hitherto incontrovertible, causing immense social and economic damage. Although the this is evident at the global level, at the local level, there is still debate about the most appropriate analyses to support this fact. This debate is particularly relevant in developing countries, such as Colombia, where there is a significant lack of data at the local level that require analysis and interpretation. Consequently, studies are often superficially conducted to support climate change theory at the local level. However, such studies are then used to design hydraulic infrastructure, with potential catastrophic errors for human and environmental health. In this study, we sought evidence of climate change through an analysis of a series of data on temperature (maximum, mean and minimum), as well as total annual and maximum rainfall in 24 h registered at the Rafael Nuñez Airport station in the city of Cartagena, Colombia, from 1941 to 2015. The hypotheses of homogeneity, trend, stationarity and non-stationarity were analyzed. Problems of non-homogeneity and the presence of periodicity in the analyzed series were found, showing a trend and apparent non-stationarity in the original series. This could be associated with the effects of climate change. In this case, no correlation was found between temperatures and rainfall. Spectral analysis was performed for all series, and residual series were generated by extracting the harmonics of greatest significance. It was found that the series data generated from the third harmonic are generally stationary and without trend. Therefore, the trend and non-stationarity of the original series are due to problems of non-homogeneity and periodicity in the series. In the results of the stationarity test conducted according to the Phillips–Perron criterion, all series were non-stationary. For the two additional criteria of stationarity tests, 40% were shown to be stationary, and 60% were non-stationary. Specifically, non-homogeneity problems and apparent trends associated with climate change could have negative implications for the design of drainage systems.
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Dissertations / Theses on the topic "Stationarity"

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Aldayel, Omar. "Evaluation of MIMO Non- Stationarity." Thesis, KTH, Signalbehandling, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-53761.

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The transmission performance of MIMO systems can be highly improved under stationary channel conditions where the channel statistics are constant. Unfortunately, mobile radio channels are not stationary all the time. Instead, they are stationary for finite time durations, so-called the stationarity regions. If these stationarity regions are relatively large, then the channel statistics can be utilized during each stationarity region to enhance the transmission performance. Therefore, it is necessary to examine the stationarity of mobile channels and characterize the stationarity regions. This thesis investigates the non-stationarity of measured MIMO channels and proposes some stationarity metrics to measure it. These metrics are: the CMD proposed by [1 ], NCMD and DES. Each one of the metrics is relevant to different types of transmission schemes and scenarios. The CMD may not be accurate for some transmission scenarios; therefore, the NCMD, which is a normalized version of CMD, is proposed. Theoretically, the NCMD can be at most 100% higher than the CMD for a 4x4 MIMO system. For beamforming scenario, the DES metric can be used to describe the non-stationarity of few eigenvectors taken from the channel variance. Under the measured MIMO channels, it was found that the CMD overestimates the stationarity regions compared to the NCMD and DES metrics particularly under the NLOS routes.
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André, Gustaf. "Testing a MIMO Channel for Stationarity." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239374.

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There is an increasing demand for better communication networks, e.g. better data rates and an improved bandwidth. As a response there is a lot of research being invested towards MIMO systems as a possible solution.In this paper data from a MIMO system with a fixed transmitter (8 antennas) and a mobile receiver (4 antennas) was investigated. The goal was to estimate the duration, during which, the MIMO system could be considered stationary. This was done by first estimating a correlation matrix for every time sample and then using the Correlation Matrix Distance function to measure how similar or dissimilar the different correlation matrices were within fixed time intervals. This was looked at from both the transmitter and receiver side. The result shows that the stationarity lasts for about 1.34 seconds and 5.2 seconds respectively for the receiver and transmitter.
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Rao, Yao. "Essays in panel stationarity and cointegration tests." Thesis, University of Liverpool, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437525.

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Pol, Marjon van der. "Intertemporal preferences for health : a comparison of the discounted utility model and hyperbolic models and of intertemporal preferences across health outcome." Thesis, University of Aberdeen, 2000. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU602020.

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It is standard practice to assume the discounted utility (DU) model on the part of the economic agents. This thesis tests the key axiom of the DU model (stationarity) in the health domain. Intertemporal preferences for health are of interest because of the debate over the appropriate treatment of future health effects in economic evaluation and of the relationship between intertemporal preferences and health-affecting behaviour. Social intertemporal preferences for fatal changes in health and private and social intertemporal preferences for non-fatal changes were elicited from members of the general public. Private intertemporal preferences for non-fatal changes were elicited from university students. Stationarity was violated in all three studies indicating that the DU model does not accurately describe individuals' intertemporal preferences. Psychologists dissatisfied with the DU model have developed hyperbolic models which replace the stationarity axiom by a generalised stationarity axiom. This thesis compared the descriptive properties of the DU model and hyperbolic discounting models in the health domain. The results showed that the hyperbolic discounting models fitted the data better than the DU model. This indicates that hyperbolic models should be preferred in the analyses of health affecting behaviour. Whether they should also be used in economic evaluations is likely to depend on other criteria as well as descriptive superiority. To inform the debate about the appropriate discount rate for health effects in economic evaluations this thesis investigated whether intertemporal preferences differ across outcomes within the health domain. The results showed that private and social intertemporal preferences for non-fatal changes in health are very similar. More differences were found between intertemporal preferences for fatal changes and non-fatal changes. This indicates that the debate over the relationship between individuals' preferences and the social discount rate is less important and that the debate should perhaps focus more on whether the rate should depend on the type of health outcome of the intervention.
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Leisch, Friedrich, Adrian Trapletti, and Kurt Hornik. "On the stationarity of autoregressive neural network models." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1998. http://epub.wu.ac.at/1612/1/document.pdf.

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We analyze the asymptotic behavior of autoregressive neural network (AR-NN) processes using techniques from Markov chains and non-linear time series analysis. It is shown that standard AR-NNs without shortcut connections are asymptotically stationary. If linear shortcut connections are allowed, only the shortcut weights determine whether the overall system is stationary, hence standard conditions for linear AR processes can be used.
Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Toalá, Enríquez Rosemberg. "Stationarity of asymptotically flat non-radiating electrovacuum spacetimes." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/89265/.

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It is proven that a solution to the Einstein-Maxwell equations whose gravitational and electromagnetic radiation fields vanish at infinity is in fact stationary in a neighbourhood of spatial infinity. That is, if in adapted coordinates the Weyl and Faraday tensors decay suitably fast and there is an asymptotically-to-all-orders Killing vector field, then this is indeed a Killing vector field in the region outside the bifurcate horizon of a sphere of sufficiently large radius. In particular, electrovacuum time-periodic spacetimes, which are truly dynamical, do not exist. This can be interpreted as a mild form of the statement: “Gravitational waves carry energy away from an isolated system". This is an extension of earlier work by Alexakis and Schlue, and Bičák, Scholtz and Tod, to include matter/energy models, in this case electromagnetism. It is also shown that the same result holds when the Einstein's equations are coupled to a massless Klein-Gordon field.
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Boyer, Alexandre. "Bidimensional stationarity of random models in the plane." Thesis, université Paris-Saclay, 2022. http://www.theses.fr/2022UPASM011.

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Dans le cadre de cette thèse,trois modèles ont été étudiés indépendamment. Ils ont en commun d’être des modèles aléatoires définis dans le plan et possédant une propriété de stationnarité bidimensionnelle. Le premier est le modèle de Hammersley stationnaire dans le quart de plan, introduit et étudié par Cator et Groeneboom.Nous présentons ici une preuve probabiliste des fluctuations gaussiennes dans le cas non critique. Le deuxième modèle peut être vu comme une version stationnaire du problème d’O’Connell-Yor. La preuve de sa stationnarité est obtenue en introduisant une discrétisation de ce modèle dont nous montrons la stationnarité, puis en observant que cette stationnarité est préservée à la limite. Enfin,le troisième modèle est une classe généralede systèmes aléatoires de lignes brisées dansle quart de plan, dont on montre la réversibilité. Cette classe contient de nombreux processus classiques comme des modèles de percolation de dernier passage. La nouveauté ici est qu’un poids est associé à chaque ligne
In this PhD thesis, three models have been independently studied. They all have in common to be random models defined in the plane and having a two-dimensional stationarity property. The first one is Hammersley’s stationary model in the quarter plane, introduced and studied by Cator and Groeneboom. We present here a probablistic proof the Gaussian fluctuations in the non-critical case. The second model can be seen as a stationary modification ofO’Connell-Yor’s problem. The proof of its stationarity is obtained by introducing a discretisation of this model, by proving its stationairty and then by observing that this stationarity is preserved in the limit. Finally, the third model is a general class of random systems of horizontal and vertical weighted broken lines on the quarter plane whose distribution are proved to be reversible. This class of systems generalizes several classical processes of the same kind. The noveltycomes here from the introduction of a weight associated with each line
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Collings, Jared M. "Clustering Methods for Delineating Regions of Spatial Stationarity." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2175.pdf.

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Chang, Kuo-Hwa. "Extreme queues and stationarity of heavy-traffic service systems." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/25441.

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Addona, Vittorio. "Stationarity in a prevalent cohort study with follow-up." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=100309.

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In a prevalent cohort study with follow-up, the incidence process is not directly observed since only the onset times of prevalent cases can be ascertained. Several important consequences follow if one can establish stationarity of the incidence process: (1) The useful epidemiological relationship between prevalence, incidence, and mean duration holds, (2) There is improved efficiency when estimating the underlying survivor function from a prevalent cohort study with follow-up, (3) The constancy of the incidence rate is established, and (4) The constant incidence rate can be estimated using data from a prevalent cohort study.
We propose a formal test for stationarity using data from a prevalent cohort study with follow-up, and establish new characterizations of stationarity, and of useful types of departure from stationarity.
A dual to the problem of establishing stationarity by comparing the backward and forward recurrence times is addressed. Assuming stationarity of the underlying incidence process, we use the backward and forward recurrence times to verify whether the underlying survival distribution is independent of the date of onset. In doing so, we characterize specific types of dependence of the underlying survival distribution on calendar time.
If the data are consistent with stationarity of the incidence rate, then a natural next step is to estimate the (constant) incidence rate. We derive the nonparametric maximum likelihood estimator of the constant incidence rate, prove that the estimator is weakly consistent, and show how one may construct an asymptotic confidence interval for the incidence rate. One main advantage of our procedure is that it only requires the completion of a single prevalent cohort study with follow-up.
We apply our test for stationarity to data obtained as part of the Canadian Study of Health and Aging to verify that the incidence rate of dementia amongst the elderly in Canada has remained constant. Upon concluding that this constancy is, plausible, we estimate the incidence rate.
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Books on the topic "Stationarity"

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Thorisson, Hermann. Coupling, Stationarity, and Regeneration. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1236-2.

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Hathaway, Neville. The non-stationarity of share price volatility. Melbourne, Aus: University of Melbourne, 1985.

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Campa, Jose. Goods arbitrage and real exchange rate stationarity. Wien: Oesterreichische Nationalbank, 1998.

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service), SpringerLink (Online, ed. Stationarity and Convergence in Reduce-or-Retreat Minimization. New York, NY: Springer New York, 2012.

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Kenny, Patrick. Non-stationarity and persistence in real exchange rates. Dublin: Department of Political Economy, UCD, 1990.

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Levy, Adam B. Stationarity and Convergence in Reduce-or-Retreat Minimization. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4642-2.

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Nåsell, Ingemar. Extinction and Quasi-Stationarity in the Stochastic Logistic SIS Model. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20530-9.

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Dahmen, E. R. Screening of hydrological data: Tests for stationarity and relative consistency. Wageningen, The Netherlands: International Institute for Land Reclamation and Improvement, 1990.

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Heveling, Matthias. Bijective point maps, point-stationarity and characterization of Palm measures. Karlsruhe: Univ.-Verl. Karlsruhe, 2006.

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Extinction and quasi-stationarity in the stochastic logistic SIS model. Heidelberg: Springer Verlag, 2011.

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Book chapters on the topic "Stationarity"

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Doukhan, Paul. "Stationarity." In Stochastic Models for Time Series, 49–70. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76938-7_4.

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Shekhar, Shashi, and Hui Xiong. "Stationarity." In Encyclopedia of GIS, 1135. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1350.

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Tsolacos, Sotiris, and Mark Andrew. "Stationarity." In Applied Quantitative Analysis for Real Estate, 222–40. Abingdon, Oxon ; New York, NY : Routledge, 2020.: Routledge, 2020. http://dx.doi.org/10.1201/9780203710876-8.

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Fouedjio, Francky. "Stationarity." In Encyclopedia of Mathematical Geosciences, 1–5. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-26050-7_428-1.

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Baccelli, François, and Pierre Brémaud. "Stationarity and Coupling." In Elements of Queueing Theory, 75–180. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-11657-9_2.

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Capasso, Vincenzo, and David Bakstein. "Stability, Stationarity, Ergodicity." In An Introduction to Continuous-Time Stochastic Processes, 281–309. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2757-9_5.

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Azencott, Robert, and Didier Dacunha-Castelle. "Forecasting and Stationarity." In Series of Irregular Observations, 25–36. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4612-4912-2_5.

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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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Levendis, John D. "Stationarity and Invertibility." In Springer Texts in Business and Economics, 81–99. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98282-3_4.

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Neusser, Klaus. "Definitions and Stationarity." In Springer Texts in Business and Economics, 201–6. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32862-1_10.

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Conference papers on the topic "Stationarity"

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Biehl, Michael, Barbara Hammer, Frank-Michael Schleif, Petra Schneider, and Thomas Villmann. "Stationarity of Matrix Relevance LVQ." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280441.

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Houdre, Christian. "Non stationarity: A new approach." In 26th IEEE Conference on Decision and Control. IEEE, 1987. http://dx.doi.org/10.1109/cdc.1987.272594.

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Borgman, Leon E. "Directional Wave Polyspectra Beyond Stationarity." In Offshore Technology Conference. Offshore Technology Conference, 1998. http://dx.doi.org/10.4043/8659-ms.

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Kukal, Jaromir, Iva Nachtigalova, Zuzana Krbcová, Jan Švihlík, and Karel Fliegel. "Stationarity testing in 2D image analysis." In Applications of Digital Image Processing XLII, edited by Andrew G. Tescher and Touradj Ebrahimi. SPIE, 2019. http://dx.doi.org/10.1117/12.2529346.

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Umansky, Dmitry, and Matthias Patzold. "Stationarity Test for Wireless Communication Channels." In GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference. IEEE, 2009. http://dx.doi.org/10.1109/glocom.2009.5425841.

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Muller, Klaus-Robert. "Multimodal imaging, non-stationarity and BCI." In 2014 International Winter Workshop on Brain-Computer Interface (BCI). IEEE, 2014. http://dx.doi.org/10.1109/iww-bci.2014.6782555.

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Galleani, Lorenzo, Leon Cohen, and Bruce Suter. "Local stationarity and time-frequency distributions." In SPIE Optics + Photonics, edited by Franklin T. Luk. SPIE, 2006. http://dx.doi.org/10.1117/12.684027.

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Seggie, D. A., J. C. Hoddinott, S. Leeman, and E. T. Costa. "Mapping Ultrasound Pulse--Echo Non-Stationarity." In Pattern Recognition and Acoustical Imaging, edited by Leonard A. Ferrari. SPIE, 1987. http://dx.doi.org/10.1117/12.940273.

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Raj, Raghu G., Alan C. Bovik, and Wilson S. Geisler. "Non-Stationarity Detection in Natural Images." In 2007 IEEE International Conference on Image Processing. IEEE, 2007. http://dx.doi.org/10.1109/icip.2007.4379307.

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Soskin, S. M. "The Kramers problem: Beyond quasi-stationarity." In Stochastic and chaotic dynamics in the lakes. AIP, 2000. http://dx.doi.org/10.1063/1.1302367.

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Reports on the topic "Stationarity"

1

Asmussen, Soeren, Peter W. Glynn, and Hermann Thorisson. Stationarity Detection in the Initial Transient Problem. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada251516.

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Lewis, Robert Michael, Virginia Joanne Torczon, and Tamara Gibson Kolda. Stationarity results for generating set search for linearly constrained optimization. Office of Scientific and Technical Information (OSTI), October 2003. http://dx.doi.org/10.2172/918255.

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Nikoukhah, Ramine, Bernard C. Levy, and Alan S. Willsky. Stability, Stochastic Stationarity and Generalized Lyapunov Equations for Two-Point Boundary-Value Descriptor Systems,. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada195645.

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Lee, Chihoon. Constrained Stochastic Differential Equations Driven by Fractional Brownian Motions: Stationarity and Parameter Estimation Problems. Fort Belvoir, VA: Defense Technical Information Center, August 2013. http://dx.doi.org/10.21236/ada591767.

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Weil, Jeffrey C., Edward G. Patton, and Peter P. Sullivan. Collaborative Research: Effects of Stability, Canopies, and Non-Stationarity on Dispersion in the Stable Boundary Layer. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada609408.

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Fowlie, Meredith, Christopher Knittel, and Catherine Wolfram. Sacred Cars? Optimal Regulation of Stationary and Non-stationary Pollution Sources. Cambridge, MA: National Bureau of Economic Research, November 2008. http://dx.doi.org/10.3386/w14504.

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Anderson, T. W. The Stationary Autogressive Model. Fort Belvoir, VA: Defense Technical Information Center, July 1986. http://dx.doi.org/10.21236/ada171416.

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Yang, Won Sik, Andrew Grandy, Andrew Boroski, Lubomir Krajtl, and Terry Johnson. Stationary Liquid Fuel Fast Reactor. Office of Scientific and Technical Information (OSTI), September 2015. http://dx.doi.org/10.2172/1227267.

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Fitzsimmons, P. J., and Michael Taksar. Stationary Regenerative Sets and Subordinators. Fort Belvoir, VA: Defense Technical Information Center, November 1986. http://dx.doi.org/10.21236/ada186298.

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Naber, Gregory L. Equivariant Localization and Stationary Phase. GIQ, 2012. http://dx.doi.org/10.7546/giq-4-2003-88-124.

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