Working Papers

[63] Marteau, C. & Sapatinas, T. (2021). Minimax testing in ill-posed inverse problems with a dictionary of operators.

[62] Marteau, C. & Sapatinas, T. (2021). Inference with unknown operator.


Publications in Journals

2022

[61] Leucht, A., Paparoditis, E., Rademacher, D. & Sapatinas, T. (2022). Testing equality of spectral density operators for functional processes. Journal of Multivariate Analysis, Vol. 189, 104889. . (Referred (online) Supplement, Journal of Multivariate Analysis, Vol. 189, 26 pages (2022). )

2020

[60] Pilavakis, D., Paparoditis, E. & Sapatinas, T. (2020). Testing equality of autocovariance operators for functional time series. Journal of Time Series Analysis, Vol. 41, 571-589. . (Referred (online) Supplement, Journal of Time Series Analysis, Vol. 41, 10 pages (2020). )

2019

[59] Pilavakis, D., Paparoditis, E. & Sapatinas, T. (2019). Moving block and tapered block bootstrap for functional time series with an application to the K-sample mean problem. Bernoulli, Vol. 25, 3496-3526. . (Referred (online) Supplement: Bernoulli, Vol. 25, 15 pages (2019). )

2017

[58] Marteau, C. & Sapatinas, T. (2017). Minimax signal detection under weak noise assumptions. Mathematical Methods of Statistics, Vol. 26, 282-298.

[57] Marteau, C. & Sapatinas, T. (2017). Minimax goodness-of-fit testing in ill-posed inverse problems with partially unknown operators. Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques, Vol. 53, 1675-1718.

2016

[56] Paparoditis, E. & Sapatinas, T. (2016). Bootstrap-based testing of equality of mean functions or equality of covariance operators for functional data. Biometrika, Vol. 103, 727-733. . (Referred (online) Supplement: Biometrika, Vol. 103, 9 pages (2016). )

2015

[55] Marteau, C. & Sapatinas, T. (2015). A unified treatment for non-asymptotic and asymptotic approaches to minimax signal detection. Statistics Surveys, Vol. 9, 253-297.

[54] Kulik, R., Sapatinas, T. & Wishart, J.R. (2015). Multichannel deconvolution with long-range dependence: Upper bounds on the Lp-risk (1 ≤ p < ∞). Applied and Computational Harmonic Analysis, Vol. 38, 357-384.

2014

[53] Benhaddou, R., Kulik, R., Pensky, M. & Sapatinas, T. (2014). Multichannel deconvolution with long-range dependence: A minimax study. Journal of Statistical Planning and Inference, Vol. 148, 1-19. (Invited Paper).

[52] Antoniadis, A., Pensky, M. & Sapatinas, T. (2014). Nonparametric regression estimation based on spatially inhomogeneous data: Minimax global convergence rates and adaptivity. ESAIM: Probability and Statistics, Vol. 18, 1-41.

[51] Cutillo, L., De Feis, I., Nikolaidou, C. & Sapatinas, T. (2014). Wavelet density estimation for weighted data. Journal of Statistical Planning and Inference, Vol. 146, 1-19.

2013

[50] Paparoditis, E. & Sapatinas, T. (2013). Short-term load forecasting: the similar shape functional time series predictor. IEEE Transactions on Power Systems, Vol. 28, 3818-3825.

2012

[49] Ingster, Yu.I., Sapatinas, T. & Suslina, I.A. (2012). Minimax signal detection in ill-posed inverse problems. Annals of Statistics, Vol. 40, 1524-1549. (Referred (online) Supplement: Annals of Statistics, Vol. 40, 43 pages (2012). )

2011

[48] Ingster, Yu.I., Sapatinas, T. & Suslina, I.A. (2011). Minimax nonparametric testing in a problem related to the Radon transform. Mathematical Methods of Statistics, Vol. 20, 347-364.

[47] Sapatinas, T., Shanbhag, D.N. & Gupta, A.K. (2011). Some new approaches to infinite divisibility. Electronic Journal of Probability, Vol. 16, 2359-2374.

[46] Pensky, M. & Sapatinas, T. (2011). Multichannel boxcar deconvolution with growing number of channels. Electronic Journal of Statistics, Vol. 5, 53-82.

[45] Petsa, A. & Sapatinas, T. (2011). On the estimation of the function and its derivatives in nonparametric regression: a Bayesian testimation approach. Sankhya, Series A, Vol. 73, 231-244.

2010

[44] Pensky, M. & Sapatinas, T. (2010). On convergence rates equivalency and sampling strategies in functional deconvolution models. Annals of Statistics, Vol. 38, 1793-1844.

[43] Abramovich, F., Grinshtein, V., Petsa, A. & Sapatinas, T. (2010). On Bayesian testimation and its applications to wavelet thresholding. Biometrika, Vol. 97, 181-198.

[42] Sapatinas, T. & Shanbhag, D.N. (2010). Moment properties of multivariate infinitely divisible laws and criteria for multivariate self-decomposability. Journal of Multivariate Analysis, Vol. 101, 500-511.

[41] Petsa, A. & Sapatinas, T. (2010). Adaptive quadratic functional estimation of a weighted density by model selection. Statistics, Vol. 44, 571-585.

2009

[40] Ingster, Yu.I. & Sapatinas, T. (2009). Minimax goodness-of-fit testing in multivariate nonparametric regression. Mathematical Methods of Statistics, Vol. 18, 241-269.

[39] Bochkina, N. & Sapatinas, T. (2009). Minimax rates of convergence and optimality of Bayes factor wavelet regression estimators under pointwise risks. Statistica Sinica, Vol. 19, 1389-1406. . (Referred (online) Supplement: Statistica Sinica, Vol. 19, 17 pages (2009). ). (Updated version of the supplement, containing minor corrections and changes. )

[38] Abramovich, F., De Feis, I. & Sapatinas, T. (2009). Optimal testing for additivity in multiple nonparametric regression. Annals of the Institute of Statistical Mathematics, Vol. 61, 691-714.

[37] Petsa, A. & Sapatinas, T. (2009). Minimax convergence rates under the Lp-risk in the functional deconvolution model. Statistics and Probability Letters, Vol. 79, 1568-1576. (Erratum: Statistics and Probability Letters, Vol. 79, 1890 (2009). )

[36] Antoniadis, A., Paparoditis, E. & Sapatinas, T. (2009). Bandwidth selection for functional time series prediction. Statistics and Probability Letters, Vol. 79, 733-740.

[35] Pensky, M. & Sapatinas, T. (2009). Functional deconvolution in a periodic setting: Uniform case. Annals of Statistics, Vol. 37, 73-104.

[34] Rao, C.R., Shanbhag, D.N., Sapatinas, T. & Rao, M.B. (2009). Some properties of extreme stable laws and related infinitely divisible random variables. Journal of Statistical Planning and Inference, Vol. 139, 802-813.

2008

[33] Fryzlewicz, P., Sapatinas, T. & Subba Rao, S. (2008). Normalised least-squares estimation in time-varying ARCH models. Annals of Statistics, Vol. 36, 742-786.

2007

[32] Michis, A.A. & Sapatinas, T. (2007). Wavelet instruments for efficiency gains in generalized method of moments models. Studies in Nonlinear Dynamics and Econometrics, Vol. 11, Issue 4, Article 4.

[31] Pensky, M. & Sapatinas, T. (2007). Frequentist optimality of Bayes factor estimators in wavelet regression models. Statistica Sinica, Vol. 17, 599-633.

[30] Antoniadis, A. & Sapatinas, T. (2007). Estimation and inference in functional mixed-effects models. Computational Statistics and Data Analysis, Vol. 51, 4793-4813.

2006

[29] Bochkina, N. & Sapatinas, T. (2006). On pointwise optimality of Bayes factor wavelet regression estimators. Sankhya, Vol. 68, 513-541.

[28] Antoniadis, A., Paparoditis, E. & Sapatinas, T. (2006). A functional wavelet-kernel approach for time series prediction. Journal of the Royal Statistical Society, Series B, Vol. 68, 837-857.

[27] Fryzlewicz, P., Sapatinas, T. & Subba Rao, S. (2006). A Haar-Fisz technique for locally stationary volatility estimation. Biometrika, Vol. 93, 687-704.

2005

[26] Bochkina, N. & Sapatinas, T. (2005). On the posterior median estimators of possibly sparse sequences. Annals of the Institute of Statistical Mathematics, Vol. 57, 315-351.

2004

[25] Abramovich, F., Antoniadis, A., Sapatinas, T. & Vidakovic, B. (2004). Optimal testing in a fixed-effects functional analysis of variance model. International Journal of Wavelets, Multiresolution and Information Processing, Vol. 2, 323-349.

[24] Angelini, C. & Sapatinas, T. (2004). Empirical Bayes approach to wavelet regression using ε-contaminated priors. Journal of Statistical Computation and Simulation, Vol. 74, 741-764.

[23] Besbeas, P., De Feis, I. & Sapatinas, T. (2004). A comparative simulation study of wavelet shrinkage estimators for Poisson counts. International Statistical Review, Vol. 72, 209-237.

[22] De Canditiis, D. & Sapatinas, T. (2004). Testing for additivity and joint effects in multivariate nonparametric regression using Fourier and wavelet methods. Statistics and Computing, Vol. 14, 235-249.

2003

[21] Antoniadis, A. & Sapatinas, T. (2003). Wavelet methods for continuous-time prediction using Hilbert-valued autoregressive processes. Journal of Multivariate Analysis, Vol. 87, 133-158.

2002

[20] Abramovich, F., Besbeas, P. & Sapatinas, T. (2002). Empirical Bayes approach to block wavelet function estimation. Computational Statistics and Data Analysis, Vol. 39, 435-451.

[19] Nason, G.P. & Sapatinas, T. (2002). Wavelet packet transfer function modelling of nonstationary time series. Statistics and Computing, Vol. 12, 45-56.

2001

[18] Nason, G.P., Sapatinas, T. & Sawczenko, A. (2001). Wavelet packet modelling of infant sleep state using heart rate data. Sankhya, Series B, Vol. 63, 199-217.

[17] Antoniadis, A., Bigot, J. & Sapatinas, T. (2001). Wavelet estimators in nonparametric regression: a comparative simulation study. Journal of Statistical Software, Vol. 6, Issue 6.

[16] Antoniadis, A., Besbeas, P. & Sapatinas, T. (2001). Wavelet shrinkage for natural exponential families with cubic variance functions. Sankhya, Series A, Vol. 63, 309-327.

[15] Antoniadis, A. & Sapatinas, T. (2001). Wavelet shrinkage for natural exponential families with quadratic variance functions. Biometrika, Vol. 88, 805-820.

2000

[14] Abramovich, F., Sapatinas, T. & Silverman, B.W. (2000). Stochastic expansions in an overcomplete wavelet dictionary. Probability Theory and Related Fields, Vol. 117, 133-144.

[13] Abramovich, F., Bailey, T.C. & Sapatinas, T. (2000). Wavelet analysis and its statistical applications. The Statistician, Vol. 49, 1-29.

1999

[12] Sapatinas, T. (1999). A characterization of the negative binomial distribution via α-monotonicity. Statistics and Probability Letters, Vol. 45, 49-53.

1998

[11] Bailey, T.C., Sapatinas, T., Powell, K.J. & Krzanowski, W.J. (1998). Signal detection in underwater sound using wavelets. Journal of the American Statistical Association, Vol. 93, 73-83.

[10] Abramovich, F., Sapatinas, T. & Silverman, B.W. (1998). Wavelet thresholding via a Bayesian approach. Journal of the Royal Statistical Society, Series B, Vol. 60, 725-749.

1996

[9] Pakes, A.G., Sapatinas, T. & Fosam, E.B. (1996). Characterizations, length-biasing, and infinite divisibility. Statistical Papers, Vol. 37, 53-69.

[8] Sapatinas, T. (1996). On the generalized Rao-Rubin condition and some variants. Australian Journal of Statistics, Vol. 38, 299-306.

1995

[7] Powell, K.J., Sapatinas, T., Bailey, T.C. & Krzanowski, W.J. (1995). Applications of wavelets to the pre-processing of underwater sounds. Statistics and Computing, Vol. 5, 265-273.

[6] Fosam, E.B. & Sapatinas, T. (1995). Characterisations of some income distributions based on multiplicative damage models. Australian Journal of Statistics, Vol. 37, 89-93.

[5] Sapatinas, T. (1995). Identifiability of mixtures of power-series distributions and related characterizations. Annals of the Institute of Statistical Mathematics, Vol. 47, 447-459.

[4] Sapatinas, T. (1995). Characterizations of probability distributions based on discrete p-monotonicity. Statistics and Probability Letters, Vol. 24, 339-344.

1994

[3] Rao, C.R., Sapatinas, T. & Shanbhag, D.N. (1994). The integrated Cauchy functional equation: some comments on recent papers. Advances in Applied Probability, Vol. 26, 825-829.

[2] Sapatinas, T. & Aly, M.A.H. (1994). Characterizations of some well-known discrete distributions based on variants of the Rao-Rubin condition. Sankhya, Series A, Vol. 56, 335-346. (Corrigendum: Sankhya, Series A, Vol. 56, 550 (1994). )

[1] Sapatinas, T. (1994). Letter to the Editor: a remark on Consul's (1990) counter-example. Communications in Statistics - Theory and Methods, Vol. 23, 2127-2128.


Publications in Books/Volumes

[2] Abramovich, F. & Sapatinas, T. (1999). Bayesian approach to wavelet decomposition and shrinkage. In Bayesian Inference in Wavelet-Based Models, Müller, P. and Vidakovic, B. (Eds.), Lecture Notes in Statistics, Vol. 141, pp. 33-50, New York: Springer-Verlag.

[1] Sapatinas, T. (1998). Log-gamma distribution. In Encyclopedia of Statistical Sciences, Kotz, S., Read, C.B. and Banks, D.L. (Eds.), Update Volume 2, pp. 372-375, New York: John Wiley & Sons.

Last update: 20th May 2022

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