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Books on the topic 'Stationarity'

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Author: Grafiati

Published: 4 June 2021

Last updated: 10 June 2023

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1

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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2

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

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3

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

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4

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

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5

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

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6

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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7

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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8

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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9

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

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10

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

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11

Schlitzer, Guiseppe. Testing stationarity of economic time series: Further Monte Carlo evidence. Rome: Banca d'Italia, 1994.

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12

Tibrewala, Vikas. A predictive test of the NBD model that controls fror non- stationarity. Fontainbleau: INSEAD, 1986.

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13

Patterson, K. D. The stationarity of data revisions: A study of some national income aggregates. Reading: University of Reading. Department of Economics, 1989.

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14

Macdonald, G. A. Testing for stationarity and co-integration: An application to Saudi-Arabian monetary data. Loughborough: Loughborough University Banking Centre, 1989.

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15

Waddell, Peter J. INTEROGATE 1.0. exploration and testing of stationarity, reversibility and clock-likeness in sequence data. Tokyo, Japan: Institute of Statistical Mathematics, 2005.

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16

Steingress, Frederick M. Stationary engineering. 4th ed. [Homewood, Ill.]: American Technical Publishers, 2008.

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17

Steingress, Frederick M. Stationary engineering. 3rd ed. Homewood, Ill: American Technical Publishers, 2003.

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18

Cavaretta, Alfred S. Stationary subdivision. Providence, R.I: American Mathematical Society, 1991.

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19

1926-, Frost Harold J., ed. Stationary engineering. 2nd ed. Homewood, Ill: American Technical Publishers, 1996.

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20

Steingress, Frederick M. Stationary engineering. Homewood, Ill: American Technical Publishers, 1991.

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21

Clements, Michael P. Forecasting with difference-stationary and trend-stationary models. Coventry: University of Warwick, Department of Economics, 1998.

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22

Segal, Mordechai. Time delay estimation in stationary and non-stationary environments. Woods Hole, Mass: Woods Hole Oceanographic Institution, 1988.

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23

Baker, Allan. Stationary steam engines. Nuneaton: A.T. Condie, 1990.

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24

Collet, Pierre, Servet Martínez, and Jaime San Martín. Quasi-Stationary Distributions. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33131-2.

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25

Gohm, Rolf. Noncommutative Stationary Processes. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b95349.

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26

Noncommutative stationary processes. Berlin: Springer, 2004.

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27

Meade, J. E. The stationary economy. New Brunswick: AldineTransaction, 2007.

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28

Petrocelly, K. L. Stationary engineering handbook. Lilburn, GA: Fairmont Press, 1989.

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29

P, Franken, and Lisek Bernd 1954-, eds. Stationary stochastic models. Chichester [England]: Wiley, 1990.

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30

Stationary bicycles: The stationary bicycle training program and buyer's guide. New York: Villard Books, 1985.

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31

Cramér, Harald. Stationary and related stochastic processes: Sample function properties and their applications. Mineola, N.Y: Dover Publications, 2004.

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32

Stat͡sionarnye sluchaĭnye prot͡sessy. 2nd ed. Moskva: "Nauka," Glav. red. fiziko-matematicheskoĭ lit-ry, 1990.

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33

Komech, Alexander, and Anatoli Merzon. Stationary Diffraction by Wedges. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26699-8.

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34

Dagalakis, Nicholas G. Coupling, Stationarity and Regeneration. Taylor & Francis Group, 2001.

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35

Danilov, V. M. Non-stationarity of Open Star Clusters. Ural University Press, 2021. http://dx.doi.org/10.15826/b978-5-7996-3173-4.

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The monograph poses issues related to the study of the non-stationarity of open star clusters (OSCs), starting with an analysis of the properties of the trajectories of individual stars to the study of collective motion of stars. A discussion of the dynamics of correlations and wave processes in such clusters is presented. The mechanisms of the dynamic evolution of OSCs, the gravitational instability of OSC nuclei, the spectra of frequencies and wavenumbers for oscillations of numerical models of OSCs, astrophysical applications of the results of studies of the dynamics of OSCs are considered. We address the monograph to academic researchers (astronomers and physicists) who are interested in astrophysics, the dynamics of stellar systems, and PhD and senior students of relevant specialities.

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36

Thorisson, Hermann. Coupling, Stationarity, and Regeneration (Probability and its Applications). Springer, 2000.

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37

Levy, Adam B. Stationarity and Convergence in Reduce-or-Retreat Minimization. Springer, 2012.

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38

Stationarity and Convergence in ReduceOrRetreat Minimization Springerbriefs in Optimization. Springer, 2012.

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39

Jankiewicz, Maria. Two stationarity conditions in the Gl/r0/M/r1 dam. Wroclaw, 1986.

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40

Nåsell, Ingemar. Extinction and Quasi-Stationarity in the Stochastic Logistic SIS Model. Springer, 2011.

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41

Schapiro, Mark. End of Stationarity: Searching for the New Normal in the Age of Carbon Shock. Chelsea Green Publishing, 2016.

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42

Schurz, Henri. Stability, stationarity, and boundedness of some implicit numerical methods for stochastic differential equations and applications. Logos, 1997.

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43

1955-, Schapiro Mark, ed. The end of stationarity: Searching for the new normal in the age of carbon shock. 2016.

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44

Mitra, Tapan. Sensitivity of Stationary Equitable Preferences. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198812555.003.0006.

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The paper studies the sensitivity implications of the class of monotone social preference orders on infinite utility streams which satisfy the axioms of Equity (Finite Anonymity) and Stationarity (Independent Future). The principal result of this investigation is that representability of such preference orders implies a certain lack of sensitivity to the utility stream of any finite number of generations, which we refer to as ‘insensitivity to the present’. Our result points to a fundamental difficulty in implementing the sustainability principle, which requires intertemporal social preferences to reflect fairly the interests of the generations in the present and in the future.

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45

McCleary, Richard, David McDowall, and Bradley J. Bartos. Noise Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0003.

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Chapter 3 introduces the Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) noise modeling strategy. The strategy begins with a test of the Normality assumption using a Kolomogov-Smirnov (KS) statistic. Non-Normal time series are transformed with a Box-Cox procedure is applied. A tentative ARIMA noise model is then identified from a sample AutoCorrelation function (ACF). If the sample ACF identifies a nonstationary model, the time series is differenced. Integer orders p and q of the underlying autoregressive and moving average structures are then identified from the ACF and partial autocorrelation function (PACF). Parameters of the tentative ARIMA noise model are estimated with maximum likelihood methods. If the estimates lie within the stationary-invertible bounds and are statistically significant, the residuals of the tentative model are diagnosed to determine whether the model’s residuals are not different than white noise. If the tentative model’s residuals satisfy this assumption, the statistically adequate model is accepted. Otherwise, the identification-estimation-diagnosis ARIMA noise model-building strategy continues iteratively until it yields a statistically adequate model. The Box-Jenkins ARIMA noise modeling strategy is illustrated with detailed analyses of twelve time series. The example analyses include non-Normal time series, stationary white noise, autoregressive and moving average time series, nonstationary time series, and seasonal time series. The time series models built in Chapter 3 are re-introduced in later chapters. Chapter 3 concludes with a discussion and demonstration of auxiliary modeling procedures that are not part of the Box-Jenkins strategy. These auxiliary procedures include the use of information criteria to compare models, unit root tests of stationarity, and co-integration.

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46

Lee, Kai Man. An empirical study of the stationarity of risk and the capital asset pricing model in the context of the Hong Kong StockMarket. 1992.

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47

Stationary. Pentagon Press, 2005.

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48

Books, Pie, and I. P. Business Stationary Graphics 2 (Business Stationary Graphics). P.I.E. Books, 1994.

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49

Stationary engineer. Syosset, N.Y: National Learning Corporation, 2001.

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50

Corporation, National Learning, ed. Stationary engineer. Syosset, N.Y: National Learning Corporation, 1994.

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