Non parametric density estimation via regularization

• Author / Creator

  Lin, Mu

• The thesis aims at showing some important methods, theories and
  applications about non-parametric density estimation via
  regularization in univariate setting.

  It gives a brief introduction to non-parametric density estimation,
  and discuss several well-known methods, for example, histogram and
  kernel methods. Regularized methods with penalization and shape
  constraints are the focus of the thesis. Maximum entropy density
  estimation is introduced and the relationship between taut string
  and maximum entropy density estimation is explored. Furthermore, the
  dual and primal theories are discussed and some theoretical proofs
  corresponding to quasi-concave density estimation are presented.
  Different the numerical methods of non-parametric density estimation
  with regularization are classified and compared. Finally, a real
  data experiment will also be discussed in the last part of the
  thesis.

• Subjects / Keywords

  • Non parametric density estimation via regularization

• Graduation date

  Fall 2009

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3N03W

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.