Time Series, or Statistics for Stochastic Processes and Dynamical Systems

09 Mar 2024 13:39

Rates of convergence of estimators; distribution-free learning theory. Large deviation techniques. Prediction schemes. Are there universal schemes which do not demand exponentially growing volumes of data?

If you have an ergodic process, then the sample-path mean for any nice statistic you care to measure will, almost surely, converge to the distributional mean. This is even true of trajectory probabilities (i.e., if you want to know the probability of a certain finite-length trajectory, simply count how often it happens). So "sit and count" is a reliable and consistent statistical procedure. If the process mixes sufficiently quickly, the rate of convergence might even be respectable. But this doesn't say anything about the efficiency of such procedures, which is surely a consideration. And what do you do for non-ergodic processes? (Take multiple runs and hope they're telling you about different ergodic components?) Non-stationary, even?

I need to learn more about frequency-domain approaches; despite being raised as a physicist, I find the time domain much more natural. After all, the frequency domain is effectively just one choice of a function basis, and there are infinitely many others, which might in some sense be more appropriate to the process at hand. But that's at least in part a rationalization against having to learn more math.

LSE econometrics and its "general-to-specific" modeling procedure is very interesting, and I think possibly even related to stuff I've done, but I need to understand it much better than I do.

(This notebook really needs subdivision.)

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  - Junichi Hirukawa and Masanobu Taniguchi, "LAN theorem for non-Gaussian locally stationary processes and its applications", Journal of Statistical Planning and Inference 136 (2006): 640--688
  - Scott H. Holan, Robert Lund, and Ginger Davis, "The ARMA alphabet soup: A tour of ARMA model variants", Statistics Surveys 4 (2010): 232--274
  - Siegfried Hörmann, "Augmented GARCH sequences: Dependence structure and asymptotics", arxiv:0805.2214
  - Jinh Hu, Wen-wen Tung, Jianbo Gao and Yinhe Cao, "Reliability of the 0-1 test for chaos", Physical Review E 72 (2005): 056207 [On Gottwald and Melbourne]
  - Jianhua Z. Huang and Lijian Yang, "Identification of Non-Linear Additive Autoregressive Models", Journal of the Royal Statistical Society B 66 (2004): 463--477 [JSTOR. Proves consistency under the assumption that the data-generating process is strictly stationary and strongly mixing.]
  - Ching-Kang Ing, "Accumulated prediction errors, information criteria and optimal forecasting for autoregressive time series", Annals of Statistics 35 (2007): 1238--1277, arxiv:0708.2373
  - Atsushi Inoue and Lutz Kilian, "In-sample or out-of-sample tests of predictability: which one should we use?", European Central Bank Working Paper [PDF]
  - Akihiko Inoue and Yukio Kasahara, "Explicit representation of finite predictor coefficients and its applications", Annals of Statistics 34 (2006): 973--993, math.ST/0405051
  - D. A. Ioannides and D. P. Papanastassiou, "Estimating the distribution function of a stationary process involving measurement errors", Statistical Inference for Stochastic Processes 4 (2001): 181--198
  - E. L. Ionides, C. Breto and A. A. King, "Inference for nonlinear dynamical systems", Proceedings of the National Academy of Sciences (USA) 103 (2006): 18438--18443
  - S. Ishii and M.-A. Sato, "Reconstruction of chaotic dynamics by on-line EM algorithm," Neural Networks 14 (2001): 1239--1256
  - Christine Jacob, "Conditional least squares estimation in nonstationary nonlinear stochastic regression models", Annals of Statistics 38 (2010): 566--597
  - Òscar Jordà, "Simultaneous Confidence Regions for Impulse Responses", The Review of Economics and Statistics 91 (2009): 629--647
  - C. T. Jose, B. Ismail, S. Jayasekhar, "Trend, Growth Rate, and Change Point Analysis: A Data Driven Approach", Communications in Statistics: Simulation and Computation 37 (2008): 498--506
  - Joseph Tadjuidje Kamgaing, Hernando Ombao and Richard A. Davis, "Autoregressive processes with data-driven regime switching", Journal of Time Series Analysis 30 (2009): 505--533
  - Matthew B. Kennel, "Testing time symmetry in time series using data compression dictionaries", Physical Review E 69 (2004): 056208
  - Igor L. Kheifets, "Specification tests for nonlinear dynamic models", arxiv:1410.3533
  - Tae Yoon Kim and Sangyeol Lee, "Kernel density estimator for strong mixing processes", Journal of Statistical Planning and Inference 133 (2005): 273--284
  - Young Min Kim, Soumendra N. Lahiri and Daniel J. Nordman, "A Progressive Block Empirical Likelihood Method for Time Series", Journal of the American Statistical Association 108 (2013): 1506--1516
  - Jon Kleinberg, "Bursty and Hierarchical Structure in Streams" [PDF]
  - D. Kleinhans, R. Friedrich, "Maximum Likelihood Estimation of Drift and Diffusion Functions", physics/0611102
  - Juanjuan Kong, Lijie Gu, Lijian Yang, "Prediction Interval for Autoregressive Time Series via Oracally Efficient Estimation of Multi‐Step‐Ahead Innovation Distribution Function", Journal of Time Series Analysis 39 (2018): 690--708
  - Hira L. Koul and Winfried Stute, "Nonparametric model checks for time series", Annals of Statistics 27 (1999): 204--236
  - Jens-Peter Kreiss, Efstathios Paparoditis, Dimitris N. Politis, "On the range of validity of the autoregressive sieve bootstrap", Annals of Statistics 39 (2011): 2103--2130, arxiv:12016211
  - Clemens Kreutz, Andreas Raue, Jens Timmer, "Likelihood based observability analysis and confidence intervals for predictions of dynamic models", arxiv:1107.0013
  - D. Kugiumtzis, "Statically Transformed Autoregressive Process and Surrogate Data Test for Nonlinearity," nlin.CD/0110025
  - Uwe Küchler and Michael Sørensen, Exponential Families of Stochastic Processes
  - Hans R. Künsch, "State Space and Hidden Markov Models", pp. 109--173 in Ole E. Barndorff-Nielsen, David R. Cox and Claudia Klüppelberg (eds.), Complex Stochastic Systems
  - Y. A. Kutoyants
    - "Goodness-of-Fit Tests for Perturbed Dynamical Systems", arxiv:0903.4612
    - "On Properties of Estimators in non Regular Situations for Poisson Processes", arxiv:0903.4613
  - B. Lacaze, "Errorless uniform sampling of complex stationary processes," Signal Processing 83 (2003): 913--917
  - Guy Lebanon, Yang Zhao, and Yanjun Zhao, "Modeling temporal text streams using the local multinomial model", Electronic Journal of Statistics 4 (2010): 566--584
  - J. Lember and A. Koloydenko, "Adjusted Viterbi training", math.ST/0406237
  - Daniel Lemire, "A Better Alternative to Piecewise Linear Time Series Segmentation", cs.DB/0605103
  - Matthieu Lerasle, "Adaptive density estimation for stationary processes", Mathematical Methods of Statistics 18 (2009): 59--83, arxiv:0909.0999
  - J. K. Lindsey, Statistical Analysis of Stochastic Processes in Time [old draft in Postscript; data and R code]
  - Yu. N. Lin'kov, Asymptotic Statistical Methods for Stochastic Processes
  - Weidong Liu and Wei Biao Wu, "Simultaneous nonparametric inference of time series", Annals of Statistics 38 (2010): 2388--2421
  - E. Locherbach, "Likelihood Ratio Processes for Markovian Particle Systems with Killing and Jumps", Statistical Inference for Stochastic Processes 5 (2002): 153--177
  - Wei-Yin Loh and Wei Zheng, "Regression trees for longitudinal and multiresponse data", Annals of Applied Statistics 7 (2013): 495--522
  - Wei Lu, Namrata Vaswani, "The Wiener-Khinchin Theorem for Non-wide Sense stationary Random Processes" ["under certain assumptions, the power spectral density (PSD) of any random process is equal to the Fourier transform of the time-averaged autocorrelation function"]
  - Xiaodong Luo, Tomomichi Nakamura and Michael Small, "Surrogate data method applied to nonlinear time series", nlin.CD/0603004
  - Xiaodong Luo, Jie Zhang, Junfeng Sun, Michael Small, Irene Moroz, "Asymptotically pivotal statistic for surrogate testing with extended hypothesis", nlin.CD/0701008
  - Xiaodong Luo, Jie Zhang and Michael Small, "Exact nonparametric inference for detection of nonlinear determinism", nlin.CD/0507049 [More exactly, this is an exact test for linear stochasticity --- rejecting the null indicates either nonlinearity or determinism, or both.]
  - Elizabeth Ann Maharaj, Pierpaolo D'Urso, and Jorge Caiado, Time Series Clustering and Classification [Review in JASA]
  - Cesar Maldonado, "Fluctuation Bounds for Chaos Plus Noise in Dynamical Systems", Journal of Statistical Physics 148 (2012): 548--564
  - Enno Mammen and Swagata Nandi, "Change of the nature of a test when surrogate data are applied", Physical Review E 70 (2004): 016121
  - Heikki Mannila and Dmitry Rusakov, "Decomposition of Event Sequences into Independent Components" [short and long versions in PS]
  - T. K. March, S. C. Chapman and R. O. Dendy, "Recurrence plot statistics and the effect of embedding", physics/0502042
  - Inés P. Mariño, Joaquín Míguez, and Riccardo Meucci, "Monte Carlo method for adaptively estimating the unknown parameters and the dynamic state of chaotic systems", Physical Review E 79 (2009): 056218
  - Pierre-Francois Marteau, "Time Warp Edit Distances with Stiffness Adjustment for Time Series Matching", cs.IR/0703033
  - Nikolai Matni, Stephen Tu, "A Tutorial on Concentration Bounds for System Identification", arxiv:1906.11395
  - Ikuo Matsuba, Hiroshi Takahashi and shinya Wakasa, "Stochastically Equivalent Dynamical System Approach to Nonlinear Deterministic Prediction", International Journal of Bifurcation and Chaos 16 (2006): 2721--2728 [I can't tell, from the abstract, if they're proposing to use stochastic systems to predict deterministic ones or vice versa; it'd be interesting either way!]
  - Muneya Matsui, "A characterization of ARMA and Fractional ARIMA models with infinitely divisible innovations", math.ST/0703731
  - Brendan P. M. McCabe, Gael M. Martin, David Harris, "Efficient probabilistic forecasts for counts", Journal of the Royal Statistical Society B 73 (2011): 253--272
  - Emma J. McCoy, Sofia C. Olhede, David A. Stephens, "Non-Regular Likelihood Inference for Seasonally Persistent Processes", arxiv:0709.0139
  - Patrick E. McSharry and Leonard A. Smith, "Consistent nonlinear dynamics: identifying model inadequacy", Physica D 192 (2004): 1--22, nlin.CD/0401024
  - Jan Mielniczuk, Zhou Zhou and Wei Biao Wu, "On nonparametric prediction of linear processes", Journal of Time Series Analysis 30 (2009): 652--673
  - Eric Moulines, Pierre Priouret and Francois Roueff, "On recursive estimation for time varying autoregressive processes", Annals of Statistics 33 (2005): 2610--2654, math.ST/0603047
  - Jose M. F. Moura and Sanjoy K. Mitter, "Identification and Filtering: Optimal Recursive Maximum Likelihood Approach" [1986 technical report from MIT; PDF preprint --- presumably long since published]
  - Hans-Georg Muller and Ulrich Stadtmuller, "Generalized functional linear models", Annals of Statistics 33 (2005): 774--805, math.ST/0505638
  - Ursula U. Müller, Anton Schick and Wolfgang Wefelmeyer, "Estimating the innovation distribution in nonparametric autoregression", Probability Theory and Related Fields 144 (2009): 53--77 ["We prove a Bahadur representation for a residual-based estimator of the innovation distribution function in a nonparametric autoregressive model. The residuals are based on a local linear smoother for the autoregression function."]
  - Tomomichi Nakamura, Xiaodong Luo, and Michael Small, "Testing for nonlinearity in time series without the Fourier transform", Physical Review E 72 (2005): 055201
  - Tomomichi Nakamura and Michael Small, "Small-shuffle surrogate data: Testing for dynamics in fluctuating data with trends", Physical Review E 72 (2005): 056216
  - Yuval Nardi, Alessandro Rinaldo, "Autoregressive Process Modeling via the Lasso Procedure", arxiv:0805.1179
  - Yoichi Nishiyama, "Goodness-of-fit test for a nonlinear time series", Journal of Time Series Analysis 30 (2009): 674--681
  - Daniel J. Nordman and Soumendra N. Lahiri, "Convergence rates of empirical block length selectors for block bootstrap", Bernoulli 20 (2014): 958-978
  - Jun Ohkubo, "Nonparametric model reconstruction for stochastic differential equations from discretely observed time-series data", Physical Review E 84 (2011): 066702
  - Jimmy Olsson, Olivier Cappe, Randal Douc and Eric Moulines, "Sequential Monte Carlo smoothing with application to parameter estimation in non-linear state space models", math.ST/0609514
  - Sorinel Adrian Oprisan, "An application of the least-squares method to system parameters extraction from experimental data", Chaos 12 (2002): 27--32
  - Brahim Ouhbi and Nikolaos Limnios, "Nonparametric estimation for semi-Markov processes based on its hazard rate functions", Statistical Inference for Stochastic Processes 2 (1999): 151--173
  - P. Palaniyandi and M. Lakshmanan, "Estimation of System Parameters and Predicting the Flow Function from Time Series of Continuous Dynamical Systems", nlin.CD/0406027
  - Milan Palus, "Coarse-grained entropy rate for characterization of complex time series", Physica D 93 (1996): 64--77 [Thanks to Prof. Palus for a reprint]
  - Angeliki Papana and Dimitris Kugiumtzis, "Evaluation of Mutual Information Estimators for Time Series", arxiv:0904.4753
  - Efstathios Paparoditis, "Validating Stationarity Assumptions in Time Series Analysis by Rolling Local Periodograms", Journal of the American Statistical Association 105 (2010): 839--851
  - Emanuel Parzen, "Statistical inference on time series by RKHS methods" (1970!)
  - Charles T. Perretti, Stephan B. Munch, and George Sugihara, "Model-free forecasting outperforms the correct mechanistic model for simulated and experimental data", Proceedings of the National Academy of Sciences 110 (2013): 5253--5257 [See also response by Hartig and Dormann]
  - Raffaella Piccarreta, "Graphical and Smoothing Techniques for Sequence Analysis", Sociological Methods and Research 41 (2012): 362--380
  - Zacharias Psaradakis
    - "A sieve bootstrap test for stationarity," Statistics and Probability Letters 62 (2003): 263--274
    - "Blockwise bootstrap testing for stationarity", Statistics and Probability Letters 76 (2006): 562--570
  - Zacharias Psaradakis, Martin Sola, Fabio Spagnolo and Nicola Spagnolo, "Selecting nonlinear time series models using information criteria", Journal of Time Series Analysis 30 (2009): 369--394
  - N. U. Prabhu and Ishawar V. Basawa (eds.), Statistical Inference in Stochastic Processes (1991)
  - B. L. S. Prakasa Rao, Semimartingales and Their Statistical
  - Ali Rahimi, Learning to Transform Time Series with a Few Examples [Ph.D. thesis, MIT dept. of electrical engineering and computer science, 2005. PDF]
  - Ali Rahimi, Ben Recht and Trevor Darrell, "Learning to Transform Time Series with a Few Examples", tech report [PDF]
  - M. B. Rajarshi, Statistical Inference for Discrete Time Stochastic Processes
  - M. M. Rao, Stochastic Processes: Inference Theory
  - Suhasini Subba Rao, "Orthogonal Samples for Estimators in Time Series", Journal of Time Series Analysis forthcoming (2018)
  - Ramiro Rico-Martinez, K. Krischer, G. Flaetgen, J.S. Anderson and I. G. Kevrekidis, "Adaptive Detection of Instabilities: An Experimental Feasibility Study," nlin.CD/0202057
  - Christoph Rieke, Ralph G. Andrzejak, Florian Mormann and Klaus Lehnertz, "Improved statistical test for nonstationarity using recurrence time statistics", Physical Review E 69 (2004): 046111 [link]
  - Ricardo Ríos, Luis-Angel Rodríguez, "Penalized estimate of the number of states in Gaussian linear AR with Markov regime", Electronic Journal of Statistics 2 (2008): 1111--1128, arxiv:0807.2726
  - John C. Robertson, Ellis W. Tallman and Charles H. Whiteman, "Forecasting using relative entropy," Federal Reserve Bank of Atlanta Working Paper 2002-20 [PDF]
  - J. W. C. Robinson, J. Rung, A. R. Bulsara and M. E. Inchiosa, "General measures for signal-noise separation in nonlinear dynamical systems," Physical Review E 63 2000: 011107
  - Brois Ryabko, "Applications of Universal Source Coding to Statistical Analysis of Time Series", arxiv:0809.1226
  - Boris Ryabko and Jaakko Astola
    - "Universal Codes as a Basis for Time Series Testing", cs.IT/0602084
    - "Universal Codes as a Basis for Nonparametric Testing of Serial Independence for Time Series", cs.IT/0506094
  - Daniil Ryabko
    - "An impossibility result for process discrimination", arxiv:0809.1053
    - "Characterizing predictable classes of processes", arxiv:0905.4341
    - "Discrimination between B-Processes is Impossible", Journal of Theoretical Probability 23 (2010): 565--575
    - "Clustering processes", arxiv:1005.0826
  - Manuel S. Santos, "Consistency properties of a simulation-based estimator for dynamic processes", Annals of Applied Probability 20 (2010): 196--213
  - Joao R. Sato, Sergi Costafreda, Pedro A. Morettin, Michael John Brammer, "Measuring Time Series Predictability Using Support Vector Regression", Communications in Statistics: Simulation and Computation 37 (2008): 1183--1197
  - Nicola Scafeta, Patti Hamilton and Paolo Grigolini, "The Thermodynamics of Social Processes: The Teen Birth Phenomenon," cond-mat/0009020 [Not because I believe them about sociology, but because they claim to have new and powerful nonparametric methods for detecting and quantifying memory in time series]
  - Francois G. Schmitt and Yongxiang Huang, Stochastic Analysis of Scaling Time Series: From Turbulence Theory to Applications
  - Thomas Schreiber and Andreas Schmitz, "Surrogate time series," chao-dyn/9909037
  - Reiner Schulz and James A. Reggia, "Temporally Asymmetric Learning Supports Sequence Processing in Multi-Winner Self-Organizing Maps", Neural Computation 16 (2004): 535--561
  - Xiaofeng Shao, "A self-normalized approach to confidence interval construction in time series", Journal of the Royal Statistical Society B 72 (2010): 343--366, arxiv:1005.2137 [Arxiv version includes an important correction to Assumption 2 and related theorems]
  - Xiaofeng Shao, Wei Biao Wu, "Asymptotic spectral theory for nonlinear time series", math.ST/0611029
  - M. Siefert, J. Peinke and R. Friedrich, "On a quantitative method to analyse dynamical and measurement noise," physics/0108034
  - A. Sitz, U. Schwarz, J. Kurths, H. U. Voss, "Estimation of parameters and unobserved components for nonlinear systems from noisy time series," Physical Review E 66 (2002): 016210
  - Michael Small and Kevin Judd, "Detecting periodicity in experimental data using linear modeling techniques", physics/9810021
  - Vadim N. Smelyanskiy and Dmitry G. Luchinsky, "Inference of stochastic nonlinear oscillators with applications to physiological problems", physics/0403121 [They present this as a Bayesian inference issue, but the core of their work appears, from skimming, to be an efficient method for computing the likelihood, so it'd apply equally well to maximum likelihood inference, for instance.]
  - V. N. Smelyanskiy, D. A. Timucin, A. Bandrivskyy and D. G. Luchinsky, "Model reconstruction of nonlinear dynamical systems driven by noise", physics/0310062
  - Dmitry A. Smirnov, Vladislav S. Vlaskin and Vladimir I. Ponomarenko, "Estimation of parameters in one-dimensional maps from noisy chaotic time series", Physics Letters A 336 (2005): 448--458
  - Eduardo D. Sontag, "For differential equations with r parameters, 2r+1 experiments are enough for identification," math.DS/0111135
  - D. Sornette and V. F. Pisarenko, "Properties of a simple bilinear stochastic model: estimation and predictability", physics/0703217
  - Ingo Steinwart, Marian Anghel, "Consistency of support vector machines for forecasting the evolution of an unknown ergodic dynamical system from observations with unknown noise", Annals of Statistics 37 (2009): 841--875, arxiv:0707.0322
  - Jean-Pierre Stockis, Jurgen Franke and Joseph Tadjuidje Kamgaing, "On geometric ergodicity of CHARME models", Journal of Time Series Analysiscite> 31 (2010): 141--152
  - Tomoya Suzuki, Tohru Ikeguchi, and Masuo Suzuki, "Effects of data windows on the methods of surrogate data", Physical Review E 71 (2005): 056708
  - Alexander G. Tartakovsky, "Asymptotic Optimality of Certain Multihypothesis Sequential Tests: Non-i.i.d. Case", Statistical Inference for Stochastic Processes 1 (1998): 265--295
  - Marco Thiel, M. Carmen Romano and Jurgen Kurths, "How much information is contained in a recurrence plot?", Physics Letters A 330 (2004): 343--349
  - Madeleine B. Thompson, "A Comparison of Methods for Computing Autocorrelation Time", arxiv:1011.0175
  - Tina Toni, David Welch, Natalja Strelkowa, Andreas Ipsen, Michael P.H. Stumpf, "Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems", arxiv:0901.1925
  - Masayuki Uchida and Nakahiro Yoshida, "Information Criteria in Model Selection for Mixing Processes", Statistical Inference for Stochastic Processes 4 (2001): 73--98
  - Yoshimasa Uematsu, "Penalized Likelihood Estimation in High-Dimensional Time Series Models and its Application", arxiv:1504.06706
  - Paolo Vidoni, "A simple procedure for computing improved prediction intervals for autoregressive models", Journal of Time Series Analysis 30 (2009): 577--590
  - Juan M. Vilar-Fernandez and Ricardo Cao, "Nonparametric Forecasting in Time Series --- A Comparative Study", Communications in Statistics: Simulation and Computation 36 (2007): 311--334
  - R. Vilela Mendes, R. Lima and T. Araujo, "A Process-Reconstruction Analysis of Market Fluctuations," cond-mat/0102301 [I don't care about the market, but they claim to have a new method for identifying distributions over entire sequences]
  - Divakar Viswanath, Xuan Liang, Kirill Serkh, "Metric Entropy and the Optimal Prediction of Chaotic Signals", arxiv:1102.3202
  - Li Wang, Lijian Yang, "Spline-backfitted kernel smoothing of nonlinear additive autoregression model", Annals of Statistics 35 (2007): 2474--2503, arxiv:math/0612677
  - Qiying Wang and Peter C. B. Phillips, "A specification test for nonlinear nonstationary models", Annals of Statistics 40 (2012): 727--758
  - Zijun Wang, "Finite Sample Performances of the Model Selection Approach in Nonparametric Model Specification for Time Series", Communications in Statistics: Theory and Methods 38 (2009): 2302--2330
  - Halbert White, Asymptotic Theory for Econometricians
  - Michael Wolf and Dan Wunderli, "Bootstrap Joint Prediction Regions", Journal of Time Series Analysis forthcoming (2014)
  - Wei Biao Wu
    
    and Jan Mielniczuk, "Kernel Density Estimation for Linear Processes", Annals of Statistics 30 (2002): 1441--1459
  - Herwig Wendt, Patrice Abry and Stephane Jaffard, "Bootstrap for Empirical Multifractal Analysis", IEEE Signal Processing Magazine July 2007, pp. 38--48 [+ technical papers by these authors]
  - Yingcun Xia, Howell Tong, "Feature Matching in Time Series Modeling", arxiv:1104.3073
  - Han Xiao and Wei Biao Wu, "Covariance matrix estimation for stationary time series", Annals of Statistics 40 (2012): 466--493
  - Chen Xu, Yao Xie, "Conformal prediction for dynamic time-series", arxiv:2010.09107
  - Hongqi Xue, Hongyu Miao, and Hulin Wu, "Sieve estimation of constant and time-varying coefficients in nonlinear ordinary differential equation models by considering both numerical error and measurement error", Annals of Statistics 38 (2010): 2351--2387
  - A. Zeileis and G. Grothendieck, "zoo: S3 Infrastructure for Regular and Irregular Time Series", Journal of Statistical Software 14 (2005): 1--27, math.ST/0505527
  - Ting Zhang, Hwai-Chung Ho, Martin Wendler, Wei Biao Wu, "Block Sampling under Strong Dependence", arxiv:1312.5807
  - Yuexu Zhao, Zhengyan Lin, "Limit theorems for kernel density estimators under dependent samples", arxiv:1305.5882
  - Zhibiao Zhao and Wei Biao Wu, "Confidence bands in nonparametric time series regression", Annals of Statistics 36 (2008): 1854--1878, arxiv:0808.1010
  - Zhou Zhou, "Nonparametric inference of quantile curves for nonstationary time series", Annals of Statistics 38 (2010): 2187--2217
  - Nicolas L. Ziebarth, Karen C. Abbott and Anthony R. Ives, "Weak population regulation in ecological time series", Ecology Letters 13 (2010): 21--31
  - M. Zukovic, D. T. Hristopulos, "Spartan Random Processes in Time Series Modeling", 0709.3418
  To write/finish:
  CRS, "Learning Rates and Recurrence Times" [a.k.a. "Wait and see"]
  CRS, "Algorithms for Inferring the Statistical Structure of Symbol Sequences: History and Review"
  Co-conspirators to be named later + CRS, "SPASM" [If I don't end up just encouraging from the sidelines...]