Active Filters

  • (-) WSL Authors = Ghosh, Rita
Search Results 1 - 20 of 63

Pages

  • RSS Feed
Select Page
On nonparametric regression for bivariate circular long-memory time series
Beran, J., Steffens, B., & Ghosh, S. (2021). On nonparametric regression for bivariate circular long-memory time series. Statistical Papers. https://doi.org/10.1007/s00362-021-01228-1
Finding exceedance locations in a large spatial database using nonparametric regression
Moser, G. E., & Ghosh, S. (2021). Finding exceedance locations in a large spatial database using nonparametric regression. Ecological Complexity, 45, 100905 (14 pp.). https://doi.org/10.1016/j.ecocom.2020.100905
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
A note on using the empirical moment generating function to estimate the variance of nonparametric trend estimates from independent time series replicates
Ghosh, S. (2020). A note on using the empirical moment generating function to estimate the variance of nonparametric trend estimates from independent time series replicates. Communications in Statistics - Simulation and Computation, 49(9), 2287-2301. https://doi.org/10.1080/03610918.2018.1516291
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
Kernel smoothing. Principles, methods and applications
Ghosh, S. (2018). Kernel smoothing. Principles, methods and applications. https://doi.org/10.1002/9781118890370
On kernel smoothing with Gaussian subordinated spatial data
Ghosh, S. (2018). On kernel smoothing with Gaussian subordinated spatial data. In P. Bertail, D. Blanke, P. A. Cornillon, & E. Matzner-Løber (Eds.), Springer proceedings in mathematics & statistics: Vol. 250. Nonparametric statistics. 3rd ISNPS, Avignon, France, June 2016 (pp. 287-293). https://doi.org/10.1007/978-3-319-96941-1_19
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
Modelling long-range dependence and trends in duration series: an approach based on EFARIMA and ESEMIFAR models
Beran, J., Feng, Y., & Ghosh, S. (2015). Modelling long-range dependence and trends in duration series: an approach based on EFARIMA and ESEMIFAR models. Statistical Papers, 56(2), 431-451. https://doi.org/10.1007/s00362-014-0590-x
On EFARIMA and ESEMIFAR models
Beran, J., Feng, Y., & Ghosh, S. (2015). On EFARIMA and ESEMIFAR models. In J. Beran, Y. Feng, & H. Hebbel (Eds.), Advanced Studies in Theoretical and Applied Econometrics. Empirical Economic and Financial Research - Theory, Methods and Practice (pp. 239-253). Springer.
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
Surface estimation under local stationarity
Ghosh, S. (2015). Surface estimation under local stationarity. Journal of Nonparametric Statistics, 27(2), 229-240. https://doi.org/10.1080/10485252.2015.1029473
Dimension reduction and data sharpening of high-dimensional vegetation data: an application to Swiss mire monitoring
Ghosh, S., Graf, U., Ecker, K., Wildi, O., Küchler, H., Feldmeyer-Christe, E., & Küchler, M. (2014). Dimension reduction and data sharpening of high-dimensional vegetation data: an application to Swiss mire monitoring. Ecological Indicators, 36, 242-253. https://doi.org/10.1016/j.ecolind.2013.07.021
On local slope estimation in partial linear models under Gaussian subordination
Ghosh, S. (2014). On local slope estimation in partial linear models under Gaussian subordination. Journal of Statistical Planning and Inference, 155, 42-53. https://doi.org/10.1016/j.jspi.2014.06.004
Long-memory processes. Probabilistic properties and statistical methods
Beran, J., Feng, Y., Ghosh, S., & Kulik, R. (2013). Long-memory processes. Probabilistic properties and statistical methods. https://doi.org/10.1007/978-3-642-35512-7
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
On trend estimation under monotone Gaussian subordination with long-memory: application to fossil pollen series
Menéndez, P., Ghosh, S., Künsch, H. R., & Tinner, W. (2013). On trend estimation under monotone Gaussian subordination with long-memory: application to fossil pollen series. Journal of Nonparametric Statistics, 25(4), 765-785. https://doi.org/10.1080/10485252.2013.826357
Emergence patterns of univoltine and bivoltine <i>Ips typographus</i> (L.) populations and associated natural enemies
Wermelinger, B., Epper, C., Kenis, M., Ghosh, S., & Holdenrieder, O. (2012). Emergence patterns of univoltine and bivoltine Ips typographus (L.) populations and associated natural enemies. Journal of Applied Entomology, 136(3), 212-224. https://doi.org/10.1111/j.1439-0418.2011.01629.x
 

Pages