Otto - Research Projects

Big Data und Machine Learning

  • Estimation of spatial dependency structures
    In spatial econometrics, the classical approach would be to replace the unknown spatial dependence structure with a linear combination of an unknown scalar, which has to be estimated, and a pre-defined matrix of spatial weights. This non-stochastic weighting matrix describes the dependence. One might obtain insight into its structure by examining the spatial covariogram or semivariogram. In practice, however, the true underlying matrix cannot easily be assessed, and therefore has to be estimated by maximizing certain goodness-of-fit measures, such as the log-likelihood, in-sample fits, information criteria, or cross validations over certain classical weighting schemes. In contrast to this classical approach, the project aims to develop methods to estimate the entire spatial weighting matrix. Moreover, the procedures should account for endogenous effects.
    Leaders: Prof. Dr. Philipp Otto
    Year: 2019
  • Spatial and spatio-temporal GARCH models
    The project is concerned with a subfield of spatial statistics, which deals in particular with the analysis of random processes in space. When analyzing such processes, it can often be found that observations that are located in spatial proximity to each other are similar. If, for example, land prices in a municipality are high, high prices can also be expected in the surrounding municipalities. In addition to this spatial dependency in the level of observations, a spatial dependency in the dispersion of observations as well as the conditional heteroskedasticity can also be determined. In the project, models for this will be developed and extended. The spatial models are analogous to the ARCH model of Robert F. Engle (1982) in time series analysis, who was awarded the Nobel Prize for Economics in 2003.
    Leaders: Prof. Dr. Philipp Otto
    Year: 2019
    Sponsors: Deutsche Forschungsgemeinschaft

Statistik

  • Detection of structural breaks in stochastic processes with spatial dependencies
    With growing availability of high-resolution spatial data, like high-definition images, 3d point clouds of LIDAR scanners, or communication and sensor networks, it might become challenging to timely detect changes and simultaneously account for spatial interactions. To detect local changes in the mean of isotropic spatiotemporal processes with a locally constraint dependence structure, we propose a monitoring procedure, which can completely be run on parallel processors. This allows for a fast detection of local changes, i.e., only a few spatial locations are affected by the change. Due to parallel computation, high-frequency data could also be monitored. We, therefore, additionally focus on the processing time required to compute the control statistics.
    Leaders: Prof. Dr. Philipp Otto
    Year: 2019
  • Spatial and spatio-temporal GARCH models
    The project is concerned with a subfield of spatial statistics, which deals in particular with the analysis of random processes in space. When analyzing such processes, it can often be found that observations that are located in spatial proximity to each other are similar. If, for example, land prices in a municipality are high, high prices can also be expected in the surrounding municipalities. In addition to this spatial dependency in the level of observations, a spatial dependency in the dispersion of observations as well as the conditional heteroskedasticity can also be determined. In the project, models for this will be developed and extended. The spatial models are analogous to the ARCH model of Robert F. Engle (1982) in time series analysis, who was awarded the Nobel Prize for Economics in 2003.
    Leaders: Prof. Dr. Philipp Otto
    Year: 2019
    Sponsors: Deutsche Forschungsgemeinschaft