Books on the topic 'Stochastic convergence'

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1

Chatterjee, Partha. Convergence in a stochastic dynamic Heckscher-Ohlin model. Ottawa: Bank of Canada, 2006.

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2

Prigent, Jean-Luc. Weak Convergence of Financial Markets. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003.

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3

A. W. van der Vaart. Weak convergence and empirical processes. New York: Springer, 1996.

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4

Weak convergence methods and singularly perturbed stochastic control and filtering problems. Boston: Birkhäuser, 1990.

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5

Bai, Jushan. Stochastic equicontinuity and weak convergence of unbounded sequential empirical processes. Cambridge, Mass: Dept. of Economics, Massachusetts Institute of Technology, 1994.

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6

Prigent, Jean-Luc. Weak convergence of financial markets: Jean-Luc Prigent. Berlin: Springer, 2003.

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7

Weak convergence of financial markets: Jean-Luc Prigent. Berlin: Springer, 2003.

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8

Lee, Kevin. Growth and convergence in a multi-country empirical stochastic Solow model. Cairo: Economic Research Forum for the Arab Countries, Iran & Turkey, 1996.

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9

Down, Douglas G. Generalized minimum variance adaptive control and parameter convergence for stochastic systems. Ottawa: National Library of Canada, 1990.

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10

Kushner, Harold J. Weak Convergence Methods and Singularly Perturbed Stochastic Control and Filtering Problems. Boston, MA: Birkhäuser Boston, 1990. http://dx.doi.org/10.1007/978-1-4612-4482-0.

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11

Yuen, Wai Kong. Applications of Cheeger's constant to the convergence rate of Markov chains on Rn. Toronto: University of Toronto, Dept. of Statistics, 1997.

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12

A Road to Randomness in Physical Systems. Berlin: Springer-Verlag, 1992.

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13

W, Evans George. Convergence of recursive learning mechanisms to steady states and cycles in stochastic nonlinear models. London: LSE Financial Markets Group, 1990.

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14

Introduction to empirical processes and semiparametric inference. New York: Springer, 2008.

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15

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

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16

Mukhopadhyay, Parimal. An Introduction To The Theory of Probability. New Jersey, USA: World Scientific Publishing Co. Inc, 2011.

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17

Pawar, Akhilesh. Probability And Statistics. New Delhi, India: Campus Books International, 2011.

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18

Saff, E. B., Douglas Patten Hardin, Brian Z. Simanek, and D. S. Lubinsky. Modern trends in constructive function theory: Conference in honor of Ed Saff's 70th birthday : constructive functions 2014, May 26-30, 2014, Vanderbilt University, Nashville, Tennessee. Providence, Rhode Island: American Mathematical Society, 2016.

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19

Lukacs, Eugene, E. Lukacs, and Z. W. Birnbaum. Stochastic Convergence. Elsevier Science & Technology Books, 2014.

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20

Pollard, D. Convergence of Stochastic Processes. Springer, 2011.

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21

Pollard, D. Convergence of Stochastic Processes. Springer London, Limited, 2012.

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22

Weak Convergence and Its Applications. World Scientific Publishing Co Pte Ltd, 2014.

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23

Mandrekar, Vidyadhar S. Weak Convergence of Stochastic Processes: With Applications to Statistical Limit Theorems. De Gruyter, Inc., 2016.

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24

Mandrekar, Vidyadhar S. Weak Convergence of Stochastic Processes: With Applications to Statistical Limit Theorems. de Gruyter GmbH, Walter, 2016.

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25

Mandrekar, Vidyadhar S. Weak Convergence of Stochastic Processes: With Applications to Statistical Limit Theorems. de Gruyter GmbH, Walter, 2016.

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26

Kushner, Harold J. Approximation and Weak Convergence Methods for Random Processes with Applications to Stochastic Systems Theory. MIT Press, 2008.

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27

Hafsa, Omar Anza, Jean Philippe Mandallena, and Gérard Michaille. Variational Convergence and Stochastic Homogenization of Nonlinear Reaction-Diffusion Problems. WORLD SCIENTIFIC, 2022. http://dx.doi.org/10.1142/12896.

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28

Variational Convergence and Stochastic Homogenization of Nonlinear Reaction-Diffusion Problems. World Scientific Pub Co Inc, 2022.

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29

Weak Convergence Methods and Singularly Perturbed Stochastic Control and Filtering Problems. Birkhäuser, 2011.

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30

Kushner, Harold. Weak Convergence Methods and Singularly Perturbed Stochastic Control and Filtering Problems. Springer, 2011.

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31

Lee, Kevin C. Growth and convergence in a multi-country empirical stochastic Solow model. University of Leicester, Department of Economics, 1996.

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32

Kushner, Harold. Weak Convergence Methods and Singularly Perturbed Stochastic Control and Filtering Problems. Birkhauser Verlag, 2012.

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33

L, Taylor Robert. Stochastic Convergence of Weighted Sums of Random Elements in Linear Spaces. Springer London, Limited, 2006.

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34

Davidson, James. Stochastic Limit Theory. 2nd ed. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844507.001.0001.

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This book aims to introduce modern asymptotic theory to students and practitioners of econometrics. It falls broadly into two parts. The first provides a handbook and reference for the underlying mathematics (Part I, Chapters 1–6), statistical theory (Part II, Chapters 7–11), and stochastic process theory (Part III, Chapters 12–18). The second half provides a treatment of the main convergence theorems used in analysing the large sample behaviour of econometric estimators and tests. These are the law of large numbers (Part IV, Chapters 19–22), the central limit theorem (Part V, Chapters 23–26), and the functional central limit theorem (Part VI, Chapters 27–32). The focus in this treatment is on the nonparametric approach to time series properties, covering topics such as nonstationarity, mixing, martingales, and near‐epoch dependence. While the approach is not elementary, care is taken to keep the treatment self‐contained. Proofs are provided for almost all the results.
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35

Kosorok, Michael R. Introduction to Empirical Processes and Semiparametric Inference. Springer New York, 2010.

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36

Weak Convergence and Empirical Processes: With Applications to Statistics (Springer Series in Statistics). 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

C, Graham, Talay D, Tubaro L. 1947-, and Centro internazionale matematico estivo, eds. Probabilistic models for nonlinear partial differential equations: Lectures given at the 1st session of the Centro internazionale matematico estivo (C.I.M.E.) held in Montecatini Terme, Italy, May 22-30, 1995. Berlin: Springer, 1996.

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39

(Editor), C. Graham, D. Talay (Editor), Th G. Kurtz (Editor), S. Meleard (Editor), Ph E. Protter (Editor), and M. Pulvirenti (Editor), eds. Probabilistic Models for Nonlinear Partial Differential Equations: Lectures Given at the 1st Session of the Centro Internazionale Matematico Estivo (C.I.M.E.) ... 22-30, 1995 (Lecture Notes in Mathematics). Springer, 1996.

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40

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

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This chapter presents some theoretical tools for defining random graph ensembles systematically via soft or hard topological constraints including working through some properties of the Erdös-Rényi random graph ensemble, which is the simplest non-trivial random graph ensemble where links appear between two nodes with a fixed probability p. The chapter sets out the central representation of graph generation as the result of a discrete-time Markovian stochastic process. This unites the two flavours of graph generation approaches – because they can be viewed as simply moving forwards or backwards through this representation. It is possible to define a random graph by an algorithm, and then calculate the associated stationary probability. The alternative approach is to specify sampling weights and then to construct an algorithm that will have these weights as the stationary probabilities upon convergence.
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41

Limit Theorems For Nonlinear Cointegrating Regression. Singapore, Hong Kong: WPSC, 2015.

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