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There are 5984 results for: content related to: Principal component analysis for interval-valued observations

  1. A resampling approach for interval-valued data regression

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 5, Issue 4, August 2012, Pages: 336–348, Jeongyoun Ahn, Muliang Peng, Cheolwoo Park and Yongho Jeon

    Article first published online : 29 MAY 2012, DOI: 10.1002/sam.11150

  2. Interval Archetypes: A New Tool for Interval Data Analysis

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 5, Issue 4, August 2012, Pages: 322–335, Maria R. D'Esposito, Francesco Palumbo and Giancarlo Ragozini

    Article first published online : 22 MAR 2012, DOI: 10.1002/sam.11140

  3. Symbolic missing data imputation in principal component analysis

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 4, Issue 2, April 2011, Pages: 171–183, Paola Zuccolotto

    Article first published online : 4 JAN 2011, DOI: 10.1002/sam.10101

  4. The quantile method for symbolic principal component analysis

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 4, Issue 2, April 2011, Pages: 184–198, Manabu Ichino

    Article first published online : 22 FEB 2011, DOI: 10.1002/sam.10111

  5. Smoothing methods for histogram-valued time series: an application to value-at-risk

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 4, Issue 2, April 2011, Pages: 216–228, Javier Arroyo, Gloria González-Rivera, Carlos Maté and Antonio Muñoz San Roque

    Article first published online : 8 MAR 2011, DOI: 10.1002/sam.10114

  6. Far beyond the classical data models: symbolic data analysis

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 4, Issue 2, April 2011, Pages: 157–170, Monique Noirhomme-Fraiture and Paula Brito

    Article first published online : 15 MAR 2011, DOI: 10.1002/sam.10112

  7. You have free access to this content
    SENIOR MIGRATION: SPATIAL CONSIDERATIONS OF AMENITY AND HEALTH ACCESS DRIVERS*

    Journal of Regional Science

    Volume 56, Issue 1, January 2016, Pages: 96–133, Jeffrey H. Dorfman and Anne M. Mandich

    Article first published online : 12 AUG 2015, DOI: 10.1111/jors.12209

  8. Relative clustering validity criteria: A comparative overview

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 3, Issue 4, August 2010, Pages: 209–235, Lucas Vendramin, Ricardo J. G. B. Campello and Eduardo R. Hruschka

    Article first published online : 30 JUN 2010, DOI: 10.1002/sam.10080

  9. A family of large margin linear classifiers and its application in dynamic environments

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 2, Issue 5-6, December 2009, Pages: 328–345, Jianqiang Shen and Thomas G. Dietterich

    Article first published online : 17 NOV 2009, DOI: 10.1002/sam.10055

  10. On the limits of clustering in high dimensions via cost functions

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 4, Issue 1, February 2011, Pages: 30–53, Hoyt A. Koepke and Bertrand S. Clarke

    Article first published online : 16 NOV 2010, DOI: 10.1002/sam.10095

  11. Testing for white noise against locally stationary alternatives

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 5, Issue 6, December 2012, Pages: 478–492, Georg M. Goerg

    Article first published online : 17 AUG 2012, DOI: 10.1002/sam.11157

  12. Fast Projection-Based Methods for the Least Squares Nonnegative Matrix Approximation Problem

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 1, Issue 1, February 2008, Pages: 38–51, Dongmin Kim, Suvrit Sra and Inderjit S. Dhillon

    Article first published online : 28 DEC 2007, DOI: 10.1002/sam.104

  13. Does Federal Aid to States Aid the States?

    Growth and Change

    Volume 45, Issue 2, June 2014, Pages: 333–361, Zachary Horváth, Brian David Moore and Jonathan C. Rork

    Article first published online : 12 FEB 2014, DOI: 10.1111/grow.12046

  14. Latent variable mining with its applications to anomalous behavior detection

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 2, Issue 1, July 2009, Pages: 70–86, Shunsuke Hirose and Kenji Yamanishi

    Article first published online : 26 MAY 2009, DOI: 10.1002/sam.10032

  15. Fast Monitoring Proximity and Centrality on Time-evolving Bipartite Graphs

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 1, Issue 3, 25 November 2008, Pages: 142–156, Hanghang Tong, Spiros Papadimitriou, Philip S. Yu and Christos Faloutsos

    Article first published online : 12 NOV 2008, DOI: 10.1002/sam.10014

  16. Predicting simulation parameters of biological systems using a Gaussian process model

    Statistical Analysis and Data Mining: The ASA Data Science Journal

    Volume 5, Issue 6, December 2012, Pages: 509–522, Xiangxin Zhu, Max Welling, Fang Jin and John Lowengrub

    Article first published online : 30 NOV 2012, DOI: 10.1002/sam.11163

  17. An Analysis of Accounting Frauds and the Timing of Analyst Coverage Decisions and Recommendation Revisions: Evidence from the US

    Journal of Business Finance & Accounting

    Volume 40, Issue 3-4, April/May 2013, Pages: 399–437, Susan M. Young and Emma Y. Peng

    Article first published online : 25 APR 2013, DOI: 10.1111/jbfa.12020

  18. Credit Spread Changes and Equity Volatility: Evidence from Daily Data

    Financial Review

    Volume 46, Issue 3, August 2011, Pages: 357–383, Ann Marie Hibbert, Ivelina Pavlova, Joel Barber and Krishnan Dandapani

    Article first published online : 7 JUL 2011, DOI: 10.1111/j.1540-6288.2011.00304.x

  19. Principal component analysis for interval data

    Wiley Interdisciplinary Reviews: Computational Statistics

    Volume 4, Issue 6, November/December 2012, Pages: 535–540, L. Billard and J. Le-Rademacher

    Article first published online : 18 SEP 2012, DOI: 10.1002/wics.1231

  20. Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?

    Journal of Forecasting

    Henning Fischer, Ángela Blanco-FERNÁndez and Peter Winker

    Article first published online : 4 NOV 2015, DOI: 10.1002/for.2371