@inproceedings{11785,
  abstract     = {{In this paper we present a novel channel impulse response estimation technique for block-oriented OFDM transmission based on combining estimators: the estimates provided by a Kalman filter operating in the time domain and a Wiener filter in the frequency domain are optimally combined by taking into account their estimated error covariances. The resulting estimator turns out to be identical to the MAP estimator of correlated jointly Gaussian mean vectors. Different variants of the proposed scheme are experimentally investigated in an EEEE 802.11a-like system setup. They compare favourably with known approaches from the literature resulting in reduced mean square estimation error and bit error rate. Further, robustness and complexity issues are discussed}},
  author       = {{Haeb-Umbach, Reinhold and Bevermeier, Maik}},
  booktitle    = {{IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)}},
  keywords     = {{bit error rate, block-oriented OFDM transmission, channel estimation, channel impulse response estimation, combining estimators, error statistics, frequency domain estimation, Gaussian mean vectors, Gaussian processes, Kalman filter, Kalman filters, MAP estimator, maximum likelihood estimation, OFDM channel estimation, OFDM modulation, time domain estimation, time-frequency analysis, Wiener filter, Wiener filters}},
  pages        = {{III--277--III--280}},
  title        = {{{OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain}}},
  doi          = {{10.1109/ICASSP.2007.366526}},
  volume       = {{3}},
  year         = {{2007}},
}

@article{11870,
  abstract     = {{We derive a class of computationally inexpensive linear dimension reduction criteria by introducing a weighted variant of the well-known K-class Fisher criterion associated with linear discriminant analysis (LDA). It can be seen that LDA weights contributions of individual class pairs according to the Euclidean distance of the respective class means. We generalize upon LDA by introducing a different weighting function}},
  author       = {{Loog, M. and Duin, R.P.W. and Haeb-Umbach, Reinhold}},
  journal      = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}},
  keywords     = {{approximate pairwise accuracy, Bayes error, Bayes methods, error statistics, Euclidean distance, Fisher criterion, linear dimension reduction, linear discriminant analysis, pattern classification, statistical analysis, statistical pattern classification, weighting function}},
  number       = {{7}},
  pages        = {{762--766}},
  title        = {{{Multiclass linear dimension reduction by weighted pairwise Fisher criteria}}},
  doi          = {{10.1109/34.935849}},
  volume       = {{23}},
  year         = {{2001}},
}

@article{40334,
  author       = {{Kitzerow, Heinz-Siegfried and Jérôme, B. and Pieranski, P.}},
  issn         = {{0378-4371}},
  journal      = {{Physica A: Statistical Mechanics and its Applications}},
  keywords     = {{Condensed Matter Physics, Statistics and Probability}},
  number       = {{1}},
  pages        = {{163--194}},
  publisher    = {{Elsevier BV}},
  title        = {{{Strain-induced anchoring transitions}}},
  doi          = {{10.1016/0378-4371(91)90423-a}},
  volume       = {{174}},
  year         = {{1991}},
}

@article{40218,
  author       = {{Lasser, R. and Rösler, Margit}},
  issn         = {{0304-4149}},
  journal      = {{Stochastic Processes and their Applications}},
  keywords     = {{Applied Mathematics, Modeling and Simulation, Statistics and Probability}},
  number       = {{2}},
  pages        = {{279--293}},
  publisher    = {{Elsevier BV}},
  title        = {{{Linear mean estimation of weakly stationary stochastic processes under the aspects of optimality and asymptotic optimality}}},
  doi          = {{10.1016/0304-4149(91)90095-t}},
  volume       = {{38}},
  year         = {{1991}},
}