| On nonparametric regression for bivariate circular long-memory time series
Beran, J., Steffens, B., & Ghosh, S. (2022). On nonparametric regression for bivariate circular long-memory time series. Statistical Papers, 63, 29-52. https://doi.org/10.1007/s00362-021-01228-1 |
| Testing for the expected number of exceedances in strongly dependent seasonal time series
Beran, J., Steffens, B., & Ghosh, S. (2021). Testing for the expected number of exceedances in strongly dependent seasonal time series. Journal of Nonparametric Statistics, 33(3-4), 417-434. https://doi.org/10.1080/10485252.2021.1977301 |
| Estimating the mean direction of strongly dependent circular time series
Beran, J., & Ghosh, S. (2020). Estimating the mean direction of strongly dependent circular time series. Journal of Time Series Analysis, 41, 210-228. https://doi.org/10.1111/jtsa.12500 |
| On aggregation of strongly dependent time series
Beran, J., Liu, H., & Ghosh, S. (2020). On aggregation of strongly dependent time series. Scandinavian Journal of Statistics, 47, 690-710. https://doi.org/10.1111/sjos.12421 |
| On local trigonometric regression under dependence
Beran, J., Steffens, B., & Ghosh, S. (2018). On local trigonometric regression under dependence. Journal of Time Series Analysis, 39(4), 592-617. https://doi.org/10.1111/jtsa.12287 |
| On estimating the marginal distribution of a detrended series with long memory
Ghosh, S. (2017). On estimating the marginal distribution of a detrended series with long memory. Communications in Statistics - Theory and Methods, 46(23), 11539-11557. https://doi.org/10.1080/03610926.2016.1275698 |
| Testing for Hermite rank in Gaussian subordination processes
Beran, J., Möhrle, S., & Ghosh, S. (2016). Testing for Hermite rank in Gaussian subordination processes. Journal of Computational and Graphical Statistics, 25(3), 917-934. https://doi.org/10.1080/10618600.2015.1056345 |
| Computation of Spatial Gini Coefficients
Ghosh, S. (2015). Computation of Spatial Gini Coefficients. Communications in Statistics - Theory and Methods, 44(22), 4709-4720. https://doi.org/10.1080/03610926.2013.823211 |
| Normality testing for a long-memory sequence using the empirical moment generating function
Ghosh, S. (2013). Normality testing for a long-memory sequence using the empirical moment generating function. Journal of Statistical Planning and Inference, 143(5), 944-954. https://doi.org/10.1016/j.jspi.2012.10.016 |
| Memory, non-stationarity and trend: analysis of environmental time series
Ghosh, S., Beran, J., Heiler, S., Percival, D., & Tinner, W. (2007). Memory, non-stationarity and trend: analysis of environmental time series. In F. Kienast, O. Wildi, & S. Ghosh (Eds.), Landscape series: Vol. 8. A changing world. Challenges for landscape research (pp. 223-247). https://doi.org/10.1007/978-1-4020-4436-6_15 |
| On estimating the cumulant generating function of linear processes
Ghosh, S., & Beran, J. (2006). On estimating the cumulant generating function of linear processes. Annals of the Institute of Statistical Mathematics, 58(1), 53-71. https://doi.org/10.1007/s10463-005-0009-5 |
| Estimating the moment generating function of a linear process
Ghosh, S. (2003). Estimating the moment generating function of a linear process. Student (Neuchâtel), 4(3), 211-218. |
| Nonparametric trend estimation in replicated time series
Ghosh, S. (2001). Nonparametric trend estimation in replicated time series. Journal of Statistical Planning and Inference, 97(2), 263-274. https://doi.org/10.1016/S0378-3758(00)00222-6 |
| Estimation of the dominating frequency for stationary and nonstationary fractional autoregressive models
Beran, J., & Ghosh, S. (2000). Estimation of the dominating frequency for stationary and nonstationary fractional autoregressive models. Journal of Time Series Analysis, 21(5), 517-533. https://doi.org/10.1111/1467-9892.00196 |
| Root-<I>n</I>-consistent estimation in partial linear models with long-memory errors
Beran, J., & Ghosh, S. (1998). Root-n-consistent estimation in partial linear models with long-memory errors. Scandinavian Journal of Statistics, 25(2), 345-357. https://doi.org/10.1111/1467-9469.00108 |
