IDEAS home Printed from https://ideas.repec.org/e/ptr51.html
   My authors  Follow this author

Kostas Triantafyllopoulos

Personal Details

First Name:Kostas
Middle Name:
Last Name:Triantafyllopoulos
Suffix:
RePEc Short-ID:ptr51
http://ktriantafyllopoulos.staff.shef.ac.uk/

Affiliation

Department of Probability and Statistics, University of Sheffield

http://www.shef.ac.uk/pas/
United Kingdon, Sheffield

Research output

as
Jump to: Working papers Articles

Working papers

  1. K. Triantafyllopoulos, 2013. "Multivariate stochastic volatility modelling using Wishart autoregressive processes," Papers 1311.0530, arXiv.org.
  2. K. Triantafyllopoulos, 2008. "Forecasting with time-varying vector autoregressive models," Papers 0802.0220, arXiv.org, revised Feb 2008.
  3. K. Triantafyllopoulos, 2008. "Multivariate stochastic volatility using state space models," Papers 0802.0223, arXiv.org.
  4. Kostas Triantafyllopoulos & Giovanni Montana, 2008. "Dynamic modeling of mean-reverting spreads for statistical arbitrage," Papers 0808.1710, arXiv.org, revised May 2009.
  5. K. Triantafyllopoulos, 2008. "Multivariate stochastic volatility with Bayesian dynamic linear models," Papers 0802.0214, arXiv.org.
  6. Kostas Triantafyllopoulos & Giovanni Montana, 2007. "Fast estimation of multivariate stochastic volatility," Papers 0708.4376, arXiv.org, revised Nov 2007.
  7. Giovanni Montana & Kostas Triantafyllopoulos & Theodoros Tsagaris, 2007. "Flexible least squares for temporal data mining and statistical arbitrage," Papers 0709.3884, arXiv.org.

Articles

  1. Sotiris Bersimis & Kostas Triantafyllopoulos, 2020. "Dynamic Non-parametric Monitoring of Air-Pollution," Methodology and Computing in Applied Probability, Springer, vol. 22(4), pages 1457-1479, December.
  2. Benjamin Kearns & Matt D. Stevenson & Kostas Triantafyllopoulos & Andrea Manca, 2019. "Generalized Linear Models for Flexible Parametric Modeling of the Hazard Function," Medical Decision Making, , vol. 39(7), pages 867-878, October.
  3. K. Triantafyllopoulos, 2012. "Multi‐variate stochastic volatility modelling using Wishart autoregressive processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(1), pages 48-60, January.
  4. K. Triantafyllopoulos, 2011. "Time-varying vector autoregressive models with stochastic volatility," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(2), pages 369-382, September.
  5. K. Triantafyllopoulos, 2011. "Real‐time covariance estimation for the local level model," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(2), pages 93-107, March.
  6. K. Triantafyllopoulos & G. Montana, 2011. "Dynamic modeling of mean-reverting spreads for statistical arbitrage," Computational Management Science, Springer, vol. 8(1), pages 23-49, April.
  7. Kostas Triantafyllopoulos, 2009. "Inference of Dynamic Generalized Linear Models: On‐Line Computation and Appraisal," International Statistical Review, International Statistical Institute, vol. 77(3), pages 430-450, December.
  8. Triantafyllopoulos, K. & Nason, G.P., 2009. "A note on state space representations of locally stationary wavelet time series," Statistics & Probability Letters, Elsevier, vol. 79(1), pages 50-54, January.
  9. Triantafyllopoulos, K., 2008. "Missing observation analysis for matrix-variate time series data," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2647-2653, November.
  10. K. Triantafyllopoulos, 2007. "Feedback quality adjustment with Bayesian state‐space models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 23(2), pages 145-156, March.
  11. K. Triantafyllopoulos, 2007. "Covariance estimation for multivariate conditionally Gaussian dynamic linear models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(8), pages 551-569.
  12. Triantafyllopoulos, K. & Nason, G.P., 2007. "A Bayesian analysis of moving average processes with time-varying parameters," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 1025-1046, October.
  13. Triantafyllopoulos, Kostas, 2006. "Multivariate discount weighted regression and local level models," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3702-3720, August.
  14. Godolphin, E.J. & Triantafyllopoulos, Kostas, 2006. "Decomposition of time series models in state-space form," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2232-2246, May.
  15. Triantafyllopoulos, Kostas & Pikoulas, John, 2002. "Multivariate Bayesian Regression Applied to the Problem of Network Security," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(8), pages 579-594, December.

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. K. Triantafyllopoulos, 2013. "Multivariate stochastic volatility modelling using Wishart autoregressive processes," Papers 1311.0530, arXiv.org.

    Cited by:

    1. Joshua Chan & Arnaud Doucet & Roberto Leon-Gonzalez & Rodney W. Strachan, 2018. "Multivariate Stochastic Volatility with Co-Heteroscedasticity," GRIPS Discussion Papers 18-12, National Graduate Institute for Policy Studies.
    2. Xin Jin & John M. Maheu, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," Working Paper series 34_14, Rimini Centre for Economic Analysis.
    3. Roberto Leon-Gonzalez, 2015. "Efficient Bayesian Inference in Generalized Inverse Gamma Processes for Stochastic Volatility," GRIPS Discussion Papers 15-17, National Graduate Institute for Policy Studies.
    4. Roberto Casarin, 2014. "A Note on Tractable State-Space Model for Symmetric Positive-Definite Matrices," Working Papers 2014:23, Department of Economics, University of Venice "Ca' Foscari".

  2. Kostas Triantafyllopoulos & Giovanni Montana, 2008. "Dynamic modeling of mean-reverting spreads for statistical arbitrage," Papers 0808.1710, arXiv.org, revised May 2009.

    Cited by:

    1. Kiseop Lee & Tim Leung & Boming Ning, 2023. "A Diversification Framework for Multiple Pairs Trading Strategies," Risks, MDPI, vol. 11(5), pages 1-18, May.
    2. Tim Leung & Brian Ward, 2015. "The golden target: analyzing the tracking performance of leveraged gold ETFs," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 32(3), pages 278-297, August.
    3. Matthew Clegg & Christopher Krauss, 2018. "Pairs trading with partial cointegration," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 121-138, January.
    4. Adrian Pizzinga & Marcelo Fernandes, 2021. "Extensions to the invariance property of maximum likelihood estimation for affine‐transformed state‐space models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(3), pages 355-371, May.
    5. Yerkin Kitapbayev & Tim Leung, 2018. "Mean Reversion Trading With Sequential Deadlines And Transaction Costs," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(01), pages 1-22, February.
    6. David S. Sun & Shih-Chuan Tsai & Wei Wang, 2013. "Behavioral Investment Strategy Matters: A Statistical Arbitrage Approach," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S3), pages 47-61, July.
    7. Trent Spears & Stefan Zohren & Stephen Roberts, 2023. "On statistical arbitrage under a conditional factor model of equity returns," Papers 2309.02205, arXiv.org.
    8. Clegg, Matthew & Krauss, Christopher, 2016. "Pairs trading with partial cointegration," FAU Discussion Papers in Economics 05/2016, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    9. Tim Leung & Xin Li, 2014. "Optimal Mean Reversion Trading with Transaction Costs and Stop-Loss Exit," Papers 1411.5062, arXiv.org, revised May 2015.
    10. Boming Ning & Prakash Chakraborty & Kiseop Lee, 2023. "Optimal Entry and Exit with Signature in Statistical Arbitrage," Papers 2309.16008, arXiv.org, revised Mar 2024.
    11. Kevin Guo & Tim Leung, 2016. "Understanding the Tracking Errors of Commodity Leveraged ETFs," Papers 1610.09404, arXiv.org.
    12. Focardi, Sergio M. & Fabozzi, Frank J. & Mitov, Ivan K., 2016. "A new approach to statistical arbitrage: Strategies based on dynamic factor models of prices and their performance," Journal of Banking & Finance, Elsevier, vol. 65(C), pages 134-155.
    13. Bolgun, Evren & Kurun, Engin & Guven, Serhat, 2009. "Dynamic Pairs Trading Strategy For The Companies Listed In The Istanbul Stock Exchange," MPRA Paper 19887, University Library of Munich, Germany.
    14. Krauss, Christopher, 2015. "Statistical arbitrage pairs trading strategies: Review and outlook," FAU Discussion Papers in Economics 09/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    15. João Frois Caldeira & Gulherme Valle Moura, 2013. "Selection of a Portfolio of Pairs Based on Cointegration: A Statistical Arbitrage Strategy," Brazilian Review of Finance, Brazilian Society of Finance, vol. 11(1), pages 49-80.
    16. Phélippé-Guinvarc'h, Martial & Cordier, Jean, 2015. "Machine Learning for Semi-Strong Efficiency Test of Inter-Market Wheat Futures," MPRA Paper 68410, University Library of Munich, Germany.
    17. Kevin Guo & Tim Leung & Brian Ward, 2019. "How to mine gold without digging," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 1-30, March.
    18. Fernando Caneo & Werner Kristjanpoller, 2021. "Improving statistical arbitrage investment strategy: Evidence from Latin American stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4424-4440, July.

  3. K. Triantafyllopoulos, 2008. "Multivariate stochastic volatility with Bayesian dynamic linear models," Papers 0802.0214, arXiv.org.

    Cited by:

    1. K. Triantafyllopoulos, 2012. "Multi‐variate stochastic volatility modelling using Wishart autoregressive processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(1), pages 48-60, January.
    2. Xu Gong & Boqiang Lin, 2018. "Structural breaks and volatility forecasting in the copper futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 290-339, March.
    3. Arthur T. Rego & Thiago R. dos Santos, 2018. "Non-Gaussian Stochastic Volatility Model with Jumps via Gibbs Sampler," Papers 1809.01501, arXiv.org, revised Oct 2018.
    4. Lopes, Hedibert F. & McCulloch, Robert E. & Tsay, Ruey S., 2022. "Parsimony inducing priors for large scale state–space models," Journal of Econometrics, Elsevier, vol. 230(1), pages 39-61.
    5. T. R. Santos, 2018. "A Bayesian GED-Gamma stochastic volatility model for return data: a marginal likelihood approach," Papers 1809.01489, arXiv.org.

  4. Giovanni Montana & Kostas Triantafyllopoulos & Theodoros Tsagaris, 2007. "Flexible least squares for temporal data mining and statistical arbitrage," Papers 0709.3884, arXiv.org.

    Cited by:

    1. Zsuzsanna Zsibók & Balázs Varga, 2012. "Inflation Persistence in Hungary: a Spatial Analysis," Working Papers 1203, Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest.
    2. Evžen Kocenda & Balázs Varga, 2017. "The Impact of Monetary Strategies on Inflation Persistence," CESifo Working Paper Series 6306, CESifo.
    3. K. Triantafyllopoulos & G. Montana, 2011. "Dynamic modeling of mean-reverting spreads for statistical arbitrage," Computational Management Science, Springer, vol. 8(1), pages 23-49, April.
    4. Zsolt Darvas & Balẳ Varga, 2014. "Inflation persistence in central and eastern European countries," Applied Economics, Taylor & Francis Journals, vol. 46(13), pages 1437-1448, May.
    5. Josipa VIŠIC & Blanka ŠKRABIC, 2010. "Determinants of Incoming Cross-Border M&A: Evidence from European Transition Economies," EcoMod2010 259600168, EcoMod.
    6. Matthew J. Lebo & Janet M. Box‐Steffensmeier, 2008. "Dynamic Conditional Correlations in Political Science," American Journal of Political Science, John Wiley & Sons, vol. 52(3), pages 688-704, July.
    7. Theodoros Tsagaris & Ajay Jasra & Niall Adams, 2010. "Robust and Adaptive Algorithms for Online Portfolio Selection," Papers 1005.2979, arXiv.org.
    8. Uliha, Gábor, 2016. "Az olajár gyengülő makrogazdasági hatásai. Két versengő elmélet szintézise [Weakening macroeconomic effects of the oil price. A synthesis of two competing theories]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(7), pages 787-818.
    9. Zsolt Darvas & Balázs Varga, 2012. "Uncovering Time-Varying Parameters with the Kalman-Filter and the Flexible Least Squares: a Monte Carlo Study," Working Papers 1204, Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest.
    10. Krauss, Christopher, 2015. "Statistical arbitrage pairs trading strategies: Review and outlook," FAU Discussion Papers in Economics 09/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    11. Sheunesu Zhou, 2021. "Examining the Sources of Sovereign Risk for South Africa: A Time Varying Flexible Least Squares Approach," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 9(1), pages 29-45.
    12. Jeff Stephenson & Bruce Vanstone & Tobias Hahn, 2021. "A Unifying Model for Statistical Arbitrage: Model Assumptions and Empirical Failure," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 943-964, December.
    13. Kuethe, Todd H. & Foster, Kenneth A. & Florax, Raymond J.G.M., 2008. "A Spatial Hedonic Model with Time-Varying Parameters: A New Method Using Flexible Least Squares," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6306, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

Articles

  1. Sotiris Bersimis & Kostas Triantafyllopoulos, 2020. "Dynamic Non-parametric Monitoring of Air-Pollution," Methodology and Computing in Applied Probability, Springer, vol. 22(4), pages 1457-1479, December.

    Cited by:

    1. Kyriakos Skarlatos & Eleni S. Bekri & Dimitrios Georgakellos & Polychronis Economou & Sotirios Bersimis, 2023. "Projecting Annual Rainfall Timeseries Using Machine Learning Techniques," Energies, MDPI, vol. 16(3), pages 1-20, February.

  2. Benjamin Kearns & Matt D. Stevenson & Kostas Triantafyllopoulos & Andrea Manca, 2019. "Generalized Linear Models for Flexible Parametric Modeling of the Hazard Function," Medical Decision Making, , vol. 39(7), pages 867-878, October.

    Cited by:

    1. Adeniyi Francis Fagbamigbe & Emma Norrman & Christina Bergh & Ulla-Britt Wennerholm & Max Petzold, 2021. "Comparison of the performances of survival analysis regression models for analysis of conception modes and risk of type-1 diabetes among 1985–2015 Swedish birth cohort," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-23, June.

  3. K. Triantafyllopoulos, 2012. "Multi‐variate stochastic volatility modelling using Wishart autoregressive processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(1), pages 48-60, January.
    See citations under working paper version above.
  4. K. Triantafyllopoulos, 2011. "Time-varying vector autoregressive models with stochastic volatility," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(2), pages 369-382, September.

    Cited by:

    1. Moura, Guilherme V. & Noriller, Mateus R., 2019. "Maximum likelihood estimation of a TVP-VAR," Economics Letters, Elsevier, vol. 174(C), pages 78-83.
    2. Schüssler, Rainer & Beckmann, Joscha & Koop, Gary & Korobilis, Dimitris, 2018. "Exchange rate predictability and dynamic Bayesian learning," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181523, Verein für Socialpolitik / German Economic Association.
    3. Choi, Ahjin & Kang, Kyu Ho, 2023. "Modeling the time-varying dynamic term structure of interest rates," Journal of Banking & Finance, Elsevier, vol. 153(C).

  5. K. Triantafyllopoulos & G. Montana, 2011. "Dynamic modeling of mean-reverting spreads for statistical arbitrage," Computational Management Science, Springer, vol. 8(1), pages 23-49, April.
    See citations under working paper version above.
  6. Kostas Triantafyllopoulos, 2009. "Inference of Dynamic Generalized Linear Models: On‐Line Computation and Appraisal," International Statistical Review, International Statistical Institute, vol. 77(3), pages 430-450, December.

    Cited by:

    1. James D. Santos & José M. J. Costa, 2019. "An Algorithm for Prior Elicitation in Dynamic Bayesian Models for Proportions with the Logit Link Function," Methodology and Computing in Applied Probability, Springer, vol. 21(1), pages 169-183, March.
    2. Mike West, 2020. "Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 1-31, February.
    3. Andrés R. Masegosa & Darío Ramos-López & Antonio Salmerón & Helge Langseth & Thomas D. Nielsen, 2020. "Variational Inference over Nonstationary Data Streams for Exponential Family Models," Mathematics, MDPI, vol. 8(11), pages 1-27, November.
    4. Benjamin Kearns & Matt D. Stevenson & Kostas Triantafyllopoulos & Andrea Manca, 2019. "Generalized Linear Models for Flexible Parametric Modeling of the Hazard Function," Medical Decision Making, , vol. 39(7), pages 867-878, October.

  7. Triantafyllopoulos, K. & Nason, G.P., 2009. "A note on state space representations of locally stationary wavelet time series," Statistics & Probability Letters, Elsevier, vol. 79(1), pages 50-54, January.

    Cited by:

    1. Antonis A. Michis & Guy P. Nason, 2017. "Case study: shipping trend estimation and prediction via multiscale variance stabilisation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(15), pages 2672-2684, November.

  8. Triantafyllopoulos, K., 2008. "Missing observation analysis for matrix-variate time series data," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2647-2653, November.

    Cited by:

    1. Sotiris Bersimis & Kostas Triantafyllopoulos, 2020. "Dynamic Non-parametric Monitoring of Air-Pollution," Methodology and Computing in Applied Probability, Springer, vol. 22(4), pages 1457-1479, December.
    2. Huashuai Qu & Ilya O. Ryzhov & Michael C. Fu & Zi Ding, 2015. "Sequential Selection with Unknown Correlation Structures," Operations Research, INFORMS, vol. 63(4), pages 931-948, August.

  9. K. Triantafyllopoulos, 2007. "Feedback quality adjustment with Bayesian state‐space models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 23(2), pages 145-156, March.

    Cited by:

    1. Sangahn Kim & Mehmet Turkoz, 2022. "Bayesian sequential update for monitoring and control of high-dimensional processes," Annals of Operations Research, Springer, vol. 317(2), pages 693-715, October.
    2. K. Triantafyllopoulos, 2008. "Multivariate stochastic volatility using state space models," Papers 0802.0223, arXiv.org.
    3. K. Triantafyllopoulos, 2008. "Multivariate stochastic volatility with Bayesian dynamic linear models," Papers 0802.0214, arXiv.org.

  10. K. Triantafyllopoulos, 2007. "Covariance estimation for multivariate conditionally Gaussian dynamic linear models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(8), pages 551-569.

    Cited by:

    1. K. Triantafyllopoulos, 2011. "Time-varying vector autoregressive models with stochastic volatility," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(2), pages 369-382, September.
    2. Sui, Yuelei & Holan, Scott H. & Yang, Wen-Hsi, 2023. "Bayesian circular lattice filters for computationally efficient estimation of multivariate time-varying autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    3. Sotiris Bersimis & Kostas Triantafyllopoulos, 2020. "Dynamic Non-parametric Monitoring of Air-Pollution," Methodology and Computing in Applied Probability, Springer, vol. 22(4), pages 1457-1479, December.
    4. Wenjie Zhao & Raquel Prado, 2020. "Efficient Bayesian PARCOR approaches for dynamic modeling of multivariate time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(6), pages 759-784, November.
    5. Ya-Ling Huang & Chin-Tsai Lin, 2011. "Developing an interval forecasting method to predict undulated demand," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(3), pages 513-524, April.

  11. Triantafyllopoulos, K. & Nason, G.P., 2007. "A Bayesian analysis of moving average processes with time-varying parameters," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 1025-1046, October.

    Cited by:

    1. Abdelkamel Alj & Christophe Ley & Guy Melard, 2015. "Asymptotic Properties of QML Estimators for VARMA Models with Time-Dependent Coefficients: Part I," Working Papers ECARES ECARES 2015-21, ULB -- Universite Libre de Bruxelles.
    2. Alj, Abdelkamel & Jónasson, Kristján & Mélard, Guy, 2016. "The exact Gaussian likelihood estimation of time-dependent VARMA models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 633-644.
    3. Triantafyllopoulos, K. & Nason, G.P., 2009. "A note on state space representations of locally stationary wavelet time series," Statistics & Probability Letters, Elsevier, vol. 79(1), pages 50-54, January.

  12. Triantafyllopoulos, Kostas, 2006. "Multivariate discount weighted regression and local level models," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3702-3720, August.

    Cited by:

    1. K. Triantafyllopoulos, 2007. "Covariance estimation for multivariate conditionally Gaussian dynamic linear models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(8), pages 551-569.
    2. Yiu‐Kuen Tse & Wai‐Sum Chan, 2010. "The Lead–Lag Relation Between The S&P500 Spot And Futures Markets: An Intraday‐Data Analysis Using A Threshold Regression Model," The Japanese Economic Review, Japanese Economic Association, vol. 61(1), pages 133-144, March.
    3. K. Triantafyllopoulos, 2008. "Multivariate stochastic volatility with Bayesian dynamic linear models," Papers 0802.0214, arXiv.org.

  13. Godolphin, E.J. & Triantafyllopoulos, Kostas, 2006. "Decomposition of time series models in state-space form," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2232-2246, May.

    Cited by:

    1. Linh Nguyen & Vilém Novák & Soheyla Mirshahi, 2020. "Trend‐cycle Estimation Using Fuzzy Transform and Its Application for Identifying Bull and Bear Phases in Markets," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(3), pages 111-124, July.
    2. Dani Gamerman & Thiago Rezende Santos & Glaura C. Franco, 2013. "A Non-Gaussian Family Of State-Space Models With Exact Marginal Likelihood," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(6), pages 625-645, November.
    3. K. Triantafyllopoulos, 2007. "Covariance estimation for multivariate conditionally Gaussian dynamic linear models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(8), pages 551-569.
    4. Proietti, Tommaso, 2007. "Signal extraction and filtering by linear semiparametric methods," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 935-958, October.
    5. Chiranjit Dutta & Nalini Ravishanker & Sumanta Basu, 2022. "Modeling Multivariate Positive-Valued Time Series Using R-INLA," Papers 2206.05374, arXiv.org, revised Jul 2022.
    6. Kostas Triantafyllopoulos, 2009. "Inference of Dynamic Generalized Linear Models: On‐Line Computation and Appraisal," International Statistical Review, International Statistical Institute, vol. 77(3), pages 430-450, December.
    7. Izquierdo, Segismundo S. & Hernández, Cesáreo & del Hoyo, Juan, 2006. "Forecasting VARMA processes using VAR models and subspace-based state space models," MPRA Paper 4235, University Library of Munich, Germany.
    8. da-Silva, C.Q. & Migon, H.S. & Correia, L.T., 2011. "Dynamic Bayesian beta models," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2074-2089, June.
    9. Vurukonda Sathish & Siuli Mukhopadhyay & Rashmi Tiwari, 2022. "Autoregressive and moving average models for zero‐inflated count time series," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(2), pages 190-218, May.

  14. Triantafyllopoulos, Kostas & Pikoulas, John, 2002. "Multivariate Bayesian Regression Applied to the Problem of Network Security," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(8), pages 579-594, December.

    Cited by:

    1. K. Triantafyllopoulos, 2007. "Covariance estimation for multivariate conditionally Gaussian dynamic linear models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(8), pages 551-569.
    2. Triantafyllopoulos, Kostas, 2006. "Multivariate discount weighted regression and local level models," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3702-3720, August.
    3. Godolphin, E.J. & Triantafyllopoulos, Kostas, 2006. "Decomposition of time series models in state-space form," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2232-2246, May.
    4. K. Triantafyllopoulos, 2008. "Multivariate stochastic volatility with Bayesian dynamic linear models," Papers 0802.0214, arXiv.org.
    5. Suvasini Panigrahi & Shamik Sural & Arun K. Majumdar, 2013. "Two-stage database intrusion detection by combining multiple evidence and belief update," Information Systems Frontiers, Springer, vol. 15(1), pages 35-53, March.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper 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-ECM: Econometrics (1) 2013-11-14
  2. NEP-ETS: Econometric Time Series (1) 2013-11-14

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Kostas Triantafyllopoulos should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.