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Elizabeth Ann Maharaj

Personal Details

First Name:Elizabeth
Middle Name:Ann
Last Name:Maharaj
Suffix:
RePEc Short-ID:pma1840
Terminal Degree:1998 Department of Econometrics and Business Statistics; Monash Business School; Monash University (from RePEc Genealogy)

Affiliation

Department of Econometrics and Business Statistics
Monash Business School
Monash University

Melbourne, Australia
http://business.monash.edu/econometrics-and-business-statistics

: 03 990 52372
03 990 55474
Room 674, Menzies Building, Wellington Road, Clayton, Victoria, 3168
RePEc:edi:dxmonau (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Maharaj, Elizabeth Ann & Alonso Fernández, Andrés Modesto, 2005. "On the comparison of time series using subsampling," DES - Working Papers. Statistics and Econometrics. WS ws050702, Universidad Carlos III de Madrid. Departamento de Estadística.
  2. Don U.A. Galagedera & Elizabeth A. Maharaj, 2004. "Wavelet timescales and conditional relationship between higher- order systematic co-moments and portfolio returns: evidence in Australian data," Finance 0409056, University Library of Munich, Germany.
  3. Elizabeth Ann Maharaj, 2003. "Using Evolutionary Spectra to Forecast Time Series," Monash Econometrics and Business Statistics Working Papers 4/03, Monash University, Department of Econometrics and Business Statistics.
  4. Maharaj, E.A., 2001. "Comparison of Non-Stationary Time Series in the Frequency Domain," Monash Econometrics and Business Statistics Working Papers 1/01, Monash University, Department of Econometrics and Business Statistics.
  5. Maharaj, E.A., 1999. "A Test for the Difference Parameter of the ARFIMA Model Using the Moving Blocks Bootstrap," Monash Econometrics and Business Statistics Working Papers 11/99, Monash University, Department of Econometrics and Business Statistics.
  6. Maharaj, E.A. & Singh, N. & Inder, B.A., 1995. "Homogeneity of Variance Test for the Comparison of Two or More Spectra," Monash Econometrics and Business Statistics Working Papers 19/95, Monash University, Department of Econometrics and Business Statistics.
  7. Maharaj, E.A., 1994. "A Significance Test for Classifying ARMA Models," Monash Econometrics and Business Statistics Working Papers 18/94, Monash University, Department of Econometrics and Business Statistics.

Articles

  1. Liu, Shen & Maharaj, Elizabeth Ann, 2013. "A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 32-49.
  2. In, Francis & Cui, Jin & Maharaj, Elizabeth Ann, 2012. "The impact of a new term auction facility on Libor–OIS spreads and volatility transmission between money and mortgage markets during the subprime crisis," Journal of International Money and Finance, Elsevier, vol. 31(5), pages 1106-1125.
  3. Tan, Pei P. & Galagedera, Don U.A. & Maharaj, Elizabeth A., 2012. "A wavelet based investigation of long memory in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2330-2341.
  4. Elizabeth Ann Maharaj & Pierpaolo D’Urso & Don Galagedera, 2010. "Wavelet-based Fuzzy Clustering of Time Series," Journal of Classification, Springer;The Classification Society, vol. 27(2), pages 231-275, September.
  5. Mala Raghavan & Jonathan Dark & Elizabeth Ann Maharaj, 2010. "Impact of capital control measures on the Malaysian stock market: A multiresolution analysis," International Journal of Managerial Finance, Emerald Group Publishing, vol. 6(2), pages 116-127, April.
  6. Maharaj, Elizabeth Ann & D’Urso, Pierpaolo, 2010. "A coherence-based approach for the pattern recognition of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3516-3537.
  7. Don Galagedera & Elizabeth Maharaj, 2008. "Wavelet timescales and conditional relationship between higher-order systematic co-moments and portfolio returns," Quantitative Finance, Taylor & Francis Journals, vol. 8(2), pages 201-215.
  8. Elizabeth A. Maharaj & Imad Moosa & Jonathan Dark & Param Silvapulle, 2008. "Wavelet Estimation of Asymmetric Hedge Ratios: Does Econometric Sophistication Boost Hedging Effectiveness?," International Journal of Business and Economics, College of Business and College of Finance, Feng Chia University, Taichung, Taiwan, vol. 7(3), pages 213-230, December.
  9. Don Galagedera & Elizabeth Maharaj & Robert Brooks, 2008. "Relationship between downside risk and return: new evidence through a multiscaling approach," Applied Financial Economics, Taylor & Francis Journals, vol. 18(20), pages 1623-1633.
  10. Robert Brooks & Elizabeth Maharaj & Breanna Pellegrini, 2008. "Estimation and analysis of the Hurst exponent for Australian stocks using wavelet analysis," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 4(1), pages 41-44.
  11. Maharaj, Elizabeth A. & Alonso, Andres M., 2007. "Discrimination of locally stationary time series using wavelets," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 879-895, October.
  12. Alonso, Andres M. & Maharaj, Elizabeth A., 2006. "Comparison of time series using subsampling," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2589-2599, June.
  13. Maharaj, Elizabeth Ann, 2002. "Comparison of non-stationary time series in the frequency domain," Computational Statistics & Data Analysis, Elsevier, vol. 40(1), pages 131-141, July.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Maharaj, E.A., 2001. "Comparison of Non-Stationary Time Series in the Frequency Domain," Monash Econometrics and Business Statistics Working Papers 1/01, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Salcedo, Gladys E. & Porto, Rogério F. & Morettin, Pedro A., 2012. "Comparing non-stationary and irregularly spaced time series," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 3921-3934.
    2. Holger Dette & Efstathios Paparoditis, 2009. "Bootstrapping frequency domain tests in multivariate time series with an application to comparing spectral densities," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(4), pages 831-857.
    3. Jentsch, Carsten & Pauly, Markus, 2012. "A note on using periodogram-based distances for comparing spectral densities," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 158-164.
    4. Sonia Díaz & José Vilar, 2010. "Comparing Several Parametric and Nonparametric Approaches to Time Series Clustering: A Simulation Study," Journal of Classification, Springer;The Classification Society, vol. 27(3), pages 333-362, November.
    5. Caiado, Jorge & Crato, Nuno, 2005. "Discrimination between deterministic trend and stochastic trend processes," MPRA Paper 2076, University Library of Munich, Germany.
    6. Preuß, Philip & Hildebrandt, Thimo, 2013. "Comparing spectral densities of stationary time series with unequal sample sizes," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 1174-1183.
    7. Dette, Holger & Paparoditis, Efstathios, 2008. "Bootstrapping frequency domain tests in multivariate time series with an application to comparing spectral densities," Technical Reports 2008,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    8. Lei Jin & Suojin Wang, 2016. "A New Test for Checking the Equality of the Correlation Structures of two time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(3), pages 355-368, May.
    9. Caiado, Jorge & Crato, Nuno & Peña, Daniel, 2007. "Comparison of time series with unequal length," MPRA Paper 6605, University Library of Munich, Germany.
    10. Caiado, Jorge & Crato, Nuno & Pena, Daniel, 2006. "A periodogram-based metric for time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2668-2684, June.
    11. Caiado, Jorge & Crato, Nuno & Peña, Daniel, 2009. "Comparison of time series with unequal length in the frequency domain," MPRA Paper 15310, University Library of Munich, Germany.
    12. Pierpaolo D’Urso & Livia Giovanni & Riccardo Massari & Dario Lallo, 2013. "Noise fuzzy clustering of time series by autoregressive metric," METRON, Springer;Sapienza Università di Roma, vol. 71(3), pages 217-243, November.
    13. Caiado, Jorge & Crato, Nuno & Peña, Daniel, 2006. "An interpolated periodogram-based metric for comparison of time series with unequal lengths," MPRA Paper 2075, University Library of Munich, Germany.
    14. Dette, Holger & Paroditis, Efstathios, 2007. "Testing equality of spectral densities," Technical Reports 2007,29, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

  2. Maharaj, E.A., 1994. "A Significance Test for Classifying ARMA Models," Monash Econometrics and Business Statistics Working Papers 18/94, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Sipan Aslan & Ceylan Yozgatligil & Cem Iyigun, 2018. "Temporal clustering of time series via threshold autoregressive models: application to commodity prices," Annals of Operations Research, Springer, vol. 260(1), pages 51-77, January.
    2. Emma SARNO & Alberto ZAZZARO, 2003. "Structural Convergence of Macroeconomic Time Series: Evidence for Inflation Rates in EU Countries," Working Papers 180, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    3. Alonso, A.M. & Berrendero, J.R. & Hernandez, A. & Justel, A., 2006. "Time series clustering based on forecast densities," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 762-776, November.
    4. Umberto Triacca, 2016. "Measuring the Distance between Sets of ARMA Models," Econometrics, MDPI, Open Access Journal, vol. 4(3), pages 1-11, July.
    5. Francesca Di Iorio & Umberto Triacca, 2014. "Testing for A Set of Linear Restrictions in VARMA Models Using Autoregressive Metric: An Application to Granger Causality Test," Econometrics, MDPI, Open Access Journal, vol. 2(4), pages 1-14, December.
    6. Maharaj, Elizabeth Ann & Alonso Fernández, Andrés Modesto, 2005. "On the comparison of time series using subsampling," DES - Working Papers. Statistics and Econometrics. WS ws050702, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Sonia Díaz & José Vilar, 2010. "Comparing Several Parametric and Nonparametric Approaches to Time Series Clustering: A Simulation Study," Journal of Classification, Springer;The Classification Society, vol. 27(3), pages 333-362, November.
    8. Anthony Bagnall & Gareth Janacek, 2014. "A Run Length Transformation for Discriminating Between Auto Regressive Time Series," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 154-178, July.
    9. Otranto, Edoardo, 2010. "Identifying financial time series with similar dynamic conditional correlation," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 1-15, January.
    10. Hirschberg, J.G. & Maasoumi, E. & Slottje, D.J., 2001. "Clusters of Attributes and Well-Being in the US," Department of Economics - Working Papers Series 778, The University of Melbourne.
    11. Di Iorio, Francesca & Triacca, Umberto, 2013. "Testing for Granger non-causality using the autoregressive metric," Economic Modelling, Elsevier, vol. 33(C), pages 120-125.
    12. Juan Vilar & José Vilar & Sonia Pértega, 2009. "Classifying Time Series Data: A Nonparametric Approach," Journal of Classification, Springer;The Classification Society, vol. 26(1), pages 3-28, April.
    13. Caiado, Jorge & Crato, Nuno & Peña, Daniel, 2007. "Comparison of time series with unequal length," MPRA Paper 6605, University Library of Munich, Germany.
    14. Alonso, Andres M. & Maharaj, Elizabeth A., 2006. "Comparison of time series using subsampling," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2589-2599, June.
    15. Marahaj, E.A. & Inder, B., 1999. "Forecasting Time Series from Clusters," Monash Econometrics and Business Statistics Working Papers 9/99, Monash University, Department of Econometrics and Business Statistics.
    16. Vilar, J.A. & Alonso, A.M. & Vilar, J.M., 2010. "Non-linear time series clustering based on non-parametric forecast densities," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2850-2865, November.
    17. Pierpaolo D’Urso & Livia Giovanni & Riccardo Massari & Dario Lallo, 2013. "Noise fuzzy clustering of time series by autoregressive metric," METRON, Springer;Sapienza Università di Roma, vol. 71(3), pages 217-243, November.
    18. E. Otranto, 2008. "Clustering Heteroskedastic Time Series by Model-Based Procedures," Working Paper CRENoS 200801, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    19. E. Otranto, 2011. "Classification of Volatility in Presence of Changes in Model Parameters," Working Paper CRENoS 201113, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    20. Giulio PALOMBA & Emma SARNO & Alberto ZAZZARO, 2007. "Testing similarities of short-run inflation dynamics among EU countries after the Euro," Working Papers 289, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    21. Corduas, Marcella & Piccolo, Domenico, 2008. "Time series clustering and classification by the autoregressive metric," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1860-1872, January.

Articles

  1. Liu, Shen & Maharaj, Elizabeth Ann, 2013. "A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 32-49.

    Cited by:

    1. Liu, Shen & Maharaj, Elizabeth Ann & Inder, Brett, 2014. "Polarization of forecast densities: A new approach to time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 345-361.
    2. Nieto-Reyes, Alicia & Cuesta-Albertos, Juan Antonio & Gamboa, Fabrice, 2014. "A random-projection based test of Gaussianity for stationary processes," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 124-141.

  2. In, Francis & Cui, Jin & Maharaj, Elizabeth Ann, 2012. "The impact of a new term auction facility on Libor–OIS spreads and volatility transmission between money and mortgage markets during the subprime crisis," Journal of International Money and Finance, Elsevier, vol. 31(5), pages 1106-1125.

    Cited by:

    1. Sheng Huang & Jonathan Williams & Ru Xie, 2017. "The Future of Money: Liquidity co-movement between financial institutions and real estate firms: evidence from China," Working Papers 17004, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    2. Helwege, Jean & Boyson, Nicole M. & Jindra, Jan, 2017. "Reprint of: Thawing frozen capital markets and backdoor bailouts: Evidence from the Fed's liquidity programs," Journal of Banking & Finance, Elsevier, vol. 83(C), pages 193-220.
    3. Codruta Maria FAT & Simona MUTU, 2014. "Analyzing The Relationship Between Eonia And Eoniaswap Rates. A Cointegration Approach," Romanian Journal of Economics, Institute of National Economy, vol. 38(1(47)), pages 197-207, June.
    4. Peña Sánchez de Rivera, Juan Ignacio & Rodríguez-Moreno, María, 2010. "Systemic risk measures: the simpler the better," DEE - Working Papers. Business Economics. WB 9291, Universidad Carlos III de Madrid. Departamento de Economía de la Empresa.
    5. Cécile Bastidon & Nicolas Huchet & Yusuf Kocoðlu, 2013. "A Second Dip in the Euro Area Money Market in 2011? Interbank Risk Premia and the ECB Bonds and Money Markets Policy," The Journal of European Theoretical and Applied Studies, The Center for European Studies at Kirklareli University - Turkey, vol. 1(1), pages 11-52.
    6. Helwege, Jean & Boyson, Nicole M. & Jindra, Jan, 2017. "Thawing frozen capital markets and backdoor bailouts: Evidence from the Fed's liquidity programs," Journal of Banking & Finance, Elsevier, vol. 76(C), pages 92-119.
    7. Cui, Jin & In, Francis & Maharaj, Elizabeth Ann, 2016. "What drives the Libor–OIS spread? Evidence from five major currency Libor–OIS spreads," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 358-375.

  3. Tan, Pei P. & Galagedera, Don U.A. & Maharaj, Elizabeth A., 2012. "A wavelet based investigation of long memory in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2330-2341.

    Cited by:

    1. John Goddard & Enrico Onali, 2016. "Long memory and multifractality: A joint test," Papers 1601.00903, arXiv.org.
    2. Chakrabarty, Anindya & De, Anupam & Gunasekaran, Angappa & Dubey, Rameshwar, 2015. "Investment horizon heterogeneity and wavelet: Overview and further research directions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 45-61.
    3. Nazarian, Rafik & Naderi, Esmaeil & Gandali Alikhani, Nadiya & Amiri, Ashkan, 2013. "Long Memory Analysis: An Empirical Investigation," MPRA Paper 45605, University Library of Munich, Germany.
    4. Lahmiri, Salim, 2015. "Long memory in international financial markets trends and short movements during 2008 financial crisis based on variational mode decomposition and detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 130-138.
    5. Huang, Shupei & An, Haizhong & Gao, Xiangyun & Huang, Xuan, 2015. "Identifying the multiscale impacts of crude oil price shocks on the stock market in China at the sector level," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 13-24.
    6. Auer, Benjamin R., 2016. "On time-varying predictability of emerging stock market returns," Emerging Markets Review, Elsevier, vol. 27(C), pages 1-13.

  4. Elizabeth Ann Maharaj & Pierpaolo D’Urso & Don Galagedera, 2010. "Wavelet-based Fuzzy Clustering of Time Series," Journal of Classification, Springer;The Classification Society, vol. 27(2), pages 231-275, September.

    Cited by:

    1. Pierpaolo D’Urso & María Ángeles Gil, 2017. "Fuzzy data analysis and classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(4), pages 645-657, December.
    2. Carolina Euán & Hernando Ombao & Joaquín Ortega, 2018. "The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 71-99, April.
    3. Pierpaolo D’Urso & Livia Giovanni & Riccardo Massari & Dario Lallo, 2013. "Noise fuzzy clustering of time series by autoregressive metric," METRON, Springer;Sapienza Università di Roma, vol. 71(3), pages 217-243, November.
    4. Angela Montanari & Daniela Calò, 2013. "Model-based clustering of probability density functions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(3), pages 301-319, September.

  5. Maharaj, Elizabeth Ann & D’Urso, Pierpaolo, 2010. "A coherence-based approach for the pattern recognition of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3516-3537.

    Cited by:

    1. Li, Hailin, 2015. "Piecewise aggregate representations and lower-bound distance functions for multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 10-25.
    2. Liu, Shen & Maharaj, Elizabeth Ann & Inder, Brett, 2014. "Polarization of forecast densities: A new approach to time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 345-361.
    3. D’Urso, Pierpaolo & Cappelli, Carmela & Di Lallo, Dario & Massari, Riccardo, 2013. "Clustering of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2114-2129.
    4. Liu, Shen & Maharaj, Elizabeth Ann, 2013. "A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 32-49.
    5. Gaunand, A. & Hocdé, A. & Lemarié, S. & Matt, M. & Turckheim, E.de, 2015. "How does public agricultural research impact society? A characterization of various patterns," Research Policy, Elsevier, vol. 44(4), pages 849-861.
    6. Eugen Scarlat, 2016. "Connectivity - Based Clustering of GDP Time Series," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 23-38, March.

  6. Don Galagedera & Elizabeth Maharaj, 2008. "Wavelet timescales and conditional relationship between higher-order systematic co-moments and portfolio returns," Quantitative Finance, Taylor & Francis Journals, vol. 8(2), pages 201-215.

    Cited by:

    1. Faria, Gonçalo & Verona, Fabio, 2016. "Forecasting stock market returns by summing the frequency-decomposed parts," Research Discussion Papers 29/2016, Bank of Finland.
    2. Faria, Gonçalo & Verona, Fabio, 2018. "The equity risk premium and the low frequency of the term spread," Research Discussion Papers 7/2018, Bank of Finland.
    3. Gonçalo Faria & Fabio Verona, 2016. "Forecasting the equity risk premium with frequency-decomposed predictors," Working Papers de Economia (Economics Working Papers) 06, Católica Porto Business School, Universidade Católica Portuguesa.
    4. Högholm, Kenneth & Knif, Johan & Koutmos, Gregory & Pynnönen, Seppo, 2011. "Distributional asymmetry of loadings on market co-moments," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(5), pages 851-866.

  7. Elizabeth A. Maharaj & Imad Moosa & Jonathan Dark & Param Silvapulle, 2008. "Wavelet Estimation of Asymmetric Hedge Ratios: Does Econometric Sophistication Boost Hedging Effectiveness?," International Journal of Business and Economics, College of Business and College of Finance, Feng Chia University, Taichung, Taiwan, vol. 7(3), pages 213-230, December.

    Cited by:

    1. Mara Madaleno & Carlos Pinho, 2010. "Hedging Performance and Multiscale Relationships in the German Electricity Spot and Futures Markets," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 3(1), pages 1-37, December.

  8. Robert Brooks & Elizabeth Maharaj & Breanna Pellegrini, 2008. "Estimation and analysis of the Hurst exponent for Australian stocks using wavelet analysis," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 4(1), pages 41-44.

    Cited by:

    1. Majumder, Debasish, 2014. "Asset pricing for inefficient markets: Evidence from China and India," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 282-291.

  9. Maharaj, Elizabeth A. & Alonso, Andres M., 2007. "Discrimination of locally stationary time series using wavelets," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 879-895, October.

    Cited by:

    1. Elizabeth Ann Maharaj & Pierpaolo D’Urso & Don Galagedera, 2010. "Wavelet-based Fuzzy Clustering of Time Series," Journal of Classification, Springer;The Classification Society, vol. 27(2), pages 231-275, September.
    2. Liu, Shen & Maharaj, Elizabeth Ann & Inder, Brett, 2014. "Polarization of forecast densities: A new approach to time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 345-361.
    3. Maharaj, Elizabeth Ann & Alonso, Andrés M., 2014. "Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 67-87.
    4. Alonso Fernández, Andrés Modesto & Maharaj, Elizabeth Ann, 2012. "Discriminant analysis of multivariate time series using wavelets," DES - Working Papers. Statistics and Econometrics. WS ws120603, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Andrés Alonso & David Casado & Sara López-Pintado & Juan Romo, 2014. "Robust Functional Supervised Classification for Time Series," Journal of Classification, Springer;The Classification Society, vol. 31(3), pages 325-350, October.
    6. Aykroyd, Robert G. & Barber, Stuart & Miller, Luke R., 2016. "Classification of multiple time signals using localized frequency characteristics applied to industrial process monitoring," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 351-362.
    7. Carolina Euán & Hernando Ombao & Joaquín Ortega, 2018. "The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 71-99, April.
    8. Romo Urroz, Juan & López Pintado, Sara & Casado, David & Alonso, Andrés M., 2008. "A functional data based method for time series classification," DES - Working Papers. Statistics and Econometrics. WS ws087427, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Liu, Shen & Maharaj, Elizabeth Ann, 2013. "A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 32-49.

  10. Alonso, Andres M. & Maharaj, Elizabeth A., 2006. "Comparison of time series using subsampling," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2589-2599, June.

    Cited by:

    1. Jin, Lei, 2011. "A data-driven test to compare two or multiple time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2183-2196, June.
    2. Maharaj, Elizabeth A. & Alonso, Andres M., 2007. "Discrimination of locally stationary time series using wavelets," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 879-895, October.
    3. Gomes, M. Ivette & Hall, Andreia & Miranda, M. Cristina, 2008. "Subsampling techniques and the Jackknife methodology in the estimation of the extremal index," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2022-2041, January.
    4. Pierpaolo D’Urso & Livia Giovanni & Riccardo Massari & Dario Lallo, 2013. "Noise fuzzy clustering of time series by autoregressive metric," METRON, Springer;Sapienza Università di Roma, vol. 71(3), pages 217-243, November.
    5. Liu, Shen & Maharaj, Elizabeth Ann, 2013. "A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 32-49.
    6. Politis, Dimitris N. & Romano, Joseph P., 2010. "K-sample subsampling in general spaces: The case of independent time series," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 316-326, February.

  11. Maharaj, Elizabeth Ann, 2002. "Comparison of non-stationary time series in the frequency domain," Computational Statistics & Data Analysis, Elsevier, vol. 40(1), pages 131-141, July.
    See citations under working paper version above.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 6 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ETS: Econometric Time Series (5) 2002-04-25 2002-04-25 2003-02-24 2004-10-18 2005-03-06. Author is listed
  2. NEP-ECM: Econometrics (2) 2002-04-25 2003-02-26
  3. NEP-FIN: Finance (2) 2004-10-18 2004-10-21
  4. NEP-RMG: Risk Management (1) 2004-10-18

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