8 research outputs found
Autocovariance estimation in regression with a discontinuous signal and -dependent errors: A difference-based approach
We discuss a class of difference-based estimators for the autocovariance in
nonparametric regression when the signal is discontinuous (change-point
regression), possibly highly fluctuating, and the errors form a stationary
-dependent process. These estimators circumvent the explicit pre-estimation
of the unknown regression function, a task which is particularly challenging
for such signals. We provide explicit expressions for their mean squared errors
when the signal function is piecewise constant (segment regression) and the
errors are Gaussian. Based on this we derive biased-optimized estimates which
do not depend on the particular (unknown) autocovariance structure. Notably,
for positively correlated errors, that part of the variance of our estimators
which depends on the signal is minimal as well. Further, we provide sufficient
conditions for -consistency; this result is extended to piecewise
Holder regression with non-Gaussian errors.
We combine our biased-optimized autocovariance estimates with a
projection-based approach and derive covariance matrix estimates, a method
which is of independent interest. Several simulation studies as well as an
application to biophysical measurements complement this paper.Comment: 41 pages, 3 figures, 3 table
Fully-Automatic Multiresolution Idealization for Filtered Ion Channel Recordings: Flickering Event Detection
We propose a new model-free segmentation method, JULES, which combines recent
statistical multiresolution techniques with local deconvolution for
idealization of ion channel recordings. The multiresolution criterion takes
into account scales down to the sampling rate enabling the detection of
flickering events, i.e., events on small temporal scales, even below the filter
frequency. For such small scales the deconvolution step allows for a precise
determination of dwell times and, in particular, of amplitude levels, a task
which is not possible with common thresholding methods. This is confirmed
theoretically and in a comprehensive simulation study. In addition, JULES can
be applied as a preprocessing method for a refined hidden Markov analysis. Our
new methodolodgy allows us to show that gramicidin A flickering events have the
same amplitude as the slow gating events. JULES is available as an R function
jules in the package clampSeg
Indice extremo en procesos heteroscedásticos
Una aplicación de la teoría de valores extremos al análisis de series de tiempo, esta es una buena manera de resumir nuestro trabajo
TATSSI: A Free and Open-Source Platform for Analyzing Earth Observation Products with Quality Data Assessment
Earth observation (EO) data play a crucial role in monitoring ecosystems and environmental processes. Time series of satellite data are essential for long-term studies in this context. Working with large volumes of satellite data, however, can still be a challenge, as the computational environment with respect to storage, processing and data handling can be demanding, which sometimes can be perceived as a barrier when using EO data for scientific purposes. In particular, open-source developments which comprise all components of EO data handling and analysis are still scarce. To overcome this difficulty, we present Tools for Analyzing Time Series of Satellite Imagery (TATSSI), an open-source platform written in Python that provides routines for downloading, generating, gap-filling, smoothing, analyzing and exporting EO time series. Since TATSSI integrates quality assessment and quality control flags when generating time series, data quality analysis is the backbone of any analysis made with the platform. We discuss TATSSI’s 3-layered architecture (data handling, engine and three application programming interfaces (API)); by allowing three APIs (a native graphical user interface, some Jupyter Notebooks and the Python command line) this development is exceptionally user-friendly. Furthermore, to demonstrate the application potential of TATSSI, we evaluated MODIS time series data for three case studies (irrigation area changes, evaluation of moisture dynamics in a wetland ecosystem and vegetation monitoring in a burned area) in different geographical regions of Mexico. Our analyses were based on methods such as the spatio-temporal distribution of maxima over time, statistical trend analysis and change-point decomposition, all of which were implemented in TATSSI. Our results are consistent with other scientific studies and results in these areas and with related in-situ data