Associate Professor Shelton Peiris

Associate Professor

F07 - Carslaw Building
The University of Sydney

Telephone 9351 5764
Fax 9351 4534

Website Shelton's homepage

Biographical details

Shelton Peiris did his PhD at Monash University, Melbourne, Victoria. After graduation from Monash in 1987, he tutored at Department of Econometrics (Monash University), lectured at the University of Moratuwa and the University of Melbourne before joining what was then the Statistics Department in 1990. He has been here ever since except for visiting appointments at the University of Waterloo (1993), the University of Manitoba (1997), the University of Iowa (1997), Thaksin University (1998), Pennsylvania State University (2002) and University of Malaya (2007, 2010, 2012/2013).

Shelton was the chair of the international program committee for international conference in Colombo, December 2011.

He was a council member and treasurer of the SSAI (Statistical Society of Australia Inc.) NSW branch (2003-2006). He has organised a number of workshops and invited paper sessions in Australia and overseas. He is an elected member of ISI. He has been the Director of Statistics Teaching Program (2005-2006) and from 2011.

He is a Sub Dean (Student Affairs) of the Faculty of Science.

Research interests

Statistical analysis of stationary and non-stationary time series data, theory and applications of estimating functions, financial time series modelling, saddlepoint and Edgeworth type apporoximations related to time series problems.

Currently, he is interests are in developing methods in Financial Econometrics and for modelling financial time series data, ACD and Log-ACD modelling and volatility modelling. A long term interest in the teaching of statistics/mathematics has also developed into a research interest with particular emphasis on the role of technology in education and learning in statistics. He is a member of the Statistics Research Group.

Associate Editor

  • Journal of Statistical Computation & Simulation (JSCS)
  • Advances of Decision Science (ADS)
  • Sri Lankan Journal of Applied Statistics (SLJAS)

Editorial Advisory Board

  • Sri Lankan Journal of Applied Statistics (SLJAS)

Selected Grants:

  • ARC Linkage Grant - A$295,000 for 2005 - 2007

Project:: Modelling Stock Market Liquidity in Australia and the Asia Pacific Region

CIs: David Allen, Michael McAleer, Shelton Peiris, Felix Chan

  • ARC Bridging Grant -A $30,000 for 2012-2013/2014

Project: Financial Duration Modelling and Forecasting for Risk Management

CIs: Richard Gerlach and Shelton Peiris

  • University of Malaya Research Grant (UMRG)- RM32900 for 2013-2014

Project: Estimation and Statistical Inference for Volatility Models in Finance

CIs: K.H.Ng, S.Peiris, W.S.Leng, K.H.Ng

  • Ministry of Higher education (MOHE) Malaysia - RM46000 for 2013-2015

Project: Monitoring Process Shifts Using Robust Control Charts

CIs: K.H.Ng, N.A.Hamzah, S.Peiris, K.H.Ng

  • University of Malaya research Grant (UMRG) - RM28000 for 2011-2012

Peroject: Estimation and Prediction Problems in GARCH Models with Non-Normal Innovations

CIs: K.H.Ng, Shelton Peiris, Pooi An Hin, Wu Swee Leng

  • University of Malaya Research Grant (UMRG) - RM 28000 for 2012-2013

Project: Estimation and Prediction Problems in GARCH Models with Non-Normal Innovations

CIs: K.H.Ng, Shelton Peiris, Pooin An Hin, Wu Swee Leng

Teaching and supervision

  • MATH1015 - Statistics for Life Science (First Year)
  • MATH1905 - Statistics Advanced (First Year)
  • STAT3011 - Time Series Analysis (Senior Level)
  • STAT3911 - Time Series Analysis (Senior Advanced Level)
  • STAT4 - Advanced Time Series Analysis and Forecasting (Honours Level)

Timetable

Current projects

  • Estimating functions and applications in stochastic modelling.
  • Second-order least squares method.
  • Generalized fractional processes.
  • State-space modelling and Kalman filtering for time series.
  • Nonlinear time series analysis.
  • Topics in Financial Econometrics

Associations

  • Statistical Society of Australia
  • Australian Mathematical Society
  • Institute of Applied Statistics, Sri Lanka

Awards and honours

  • 2012: Faculty of Science Teaching Citation for Excellent Teaching
  • 2011: Bronz Medal for Research - University Putra Malaysia (UPM, With M. Shitan)
  • 2007: Bronz Medal for Research - UPM (With M. Shitan)
  • 2007: Bronz Medal for Research - UPM (With M. Shitan)
  • 1983: Monash University Graduate Scholarship

International links

Canada

(University of Manitoba) Visiting Professor/Time Series Research

Indonesia

(University of Gajamadah) Research projects on statistical analysis of time series anf financial econometrics

Malaysia

(University of Malaya) Visiting Professor/Research in Financial Econometrics

Selected publications

Download citations: PDF RTF Endnote

Books

  • Rosner, B., Peiris, M., Chan, J., Marchev, D. (2013). MATH1015: Biostatistics. Sydney: Cengage Learning.

Journals

  • Ng, K., Peiris, M., Gerlach, R. (2014). Estimation and forecasting with logarithmic autoregressive conditional duration models: A comparative study with an application. Expert Systems with Applications, 41(7), 3323-3332. [More Information]
  • Rosadi, D., Peiris, M. (2014). Second-order least-squares estimation for regression models with autocorrelated errors. Computational Statistics, 29(5), 931-943. [More Information]
  • Peiris, M. (2014). Testing the null hypothesis of zero serial correlation in short panel time series: a comparison of tail probabilities. Statistical Papers, 55(2), 513-523. [More Information]
  • Shitan, M., Peiris, M. (2013). Approximate Asymptotic Variance-Covariance Matrix for the Whittle Estimators of GAR(1) Parameters. Communications in Statistics - Theory and Methods, 42(5), 756-770. [More Information]
  • Peiris, M. (2013). Efficient Estimation of Regression Models with Heteroscedastic Errors. Mathematical Scientist, 38, 124-128.
  • Allen, D., Ng, K., Peiris, M. (2013). Estimating and simulating Weibull models of risk or price durations: An application to ACD models. The North American Journal of Economics and Finance, 25, 214-225. [More Information]
  • Ng, K., Peiris, M. (2013). Modelling High Frequency Transaction Data in Financial Economics: A Comparative Study Based on Simulations. Journal of Economic Computation and Economic Cybernetics Studies and Research, 47(2), 189-201.
  • Allen, D., Ng, K., Peiris, M. (2013). The efficient modelling of high frequency transaction data: A new application of estimating functions in financial economics. Economics Letters, 120, 117-122. [More Information]
  • Pillai, T., Shitan, M., Peiris, M. (2012). Some Properties of the Generalized Autoregressive Moving Average (GARMA (1, 1; 1, 2)) Model. Communications in Statistics - Theory and Methods, 41(4), 699-716. [More Information]
  • Abdullah, N., Mohammed, I., Peiris, M., Azizan, A. (2011). A New Iterative Procedure for Estimation of RCA Parameters Based on Estimating Functions. Applied Mathematical Sciences, 5(4), 193-202.
  • Peiris, M., Thavaneswaran, A., Appadoo, S. (2011). Doubly stochastic models with GARCH innovations. Applied Mathematics Letters, 24(11), 1768-1773. [More Information]
  • Dissanayake, G., Peiris, M. (2011). Generalized Fractional Processes with Conditional Heteroscedasticity. Sri Lankan Journal of Applied Statistics, 12(Special Issue 2011), 1-12.
  • Shitan, M., Peiris, M. (2011). Time Series Properties of the Class of Generalized First-Order Autoregressive Processes with Moving Average Errors. Communications in Statistics - Theory and Methods, 40(13), 2259-2275. [More Information]
  • Shitan, M., Peiris, M. (2009). A Note on the properties of generalised separable spatial autoregressive process. Journal of Probability and Statistics, 2009, 847830-1-847830-11.
  • Allen, D., Lazarov, Z., McAleer, M., Peiris, M. (2009). Comparison of Alternative ACD Models via density and interval forecasts: Evidence from the Australian Stock Market. Mathematics and Computers in Simulation, 79(8), 2535-2555.
  • Pathmanathan, N., Ng, K., Peiris, M. (2009). On Estimation of Autoregressive Conditional Duration (ACD) Models Based on Different Error Distributions. Sri Lankan Journal of Applied Statistics, 10, 251-269.
  • Shitan, M., Peiris, M. (2009). On properties of the second order generalized autoregressive GAR(2) model with index. Mathematics and Computers in Simulation, 80(2), 367-377.
  • Pillai, T., Shitan, M., Peiris, M. (2009). Time series properties of the class of first order autoregressive processes with generalized moving average errors. Journal of Statistics: Advances in theory and applications, 2(1), 71-92.
  • Thavaneswaran, A., Peiris, M., Singh, J. (2008). Derivation of Kurtosis and option price formulae for popular volatility models with applications in finance. Communications in Statistics - Theory and Methods, 37(11), 1799-1814.
  • Allen, D., Chan, F., McAleer, M., Peiris, M. (2008). Finite sample properties of the QMLE for the Log-ACD model: Application to Australian stocks. Journal of Econometrics, 147(1), 163-185. [More Information]
  • Shitan, M., Peiris, M. (2008). Generalized autoregressive (GAR) model: A comparison of maximum likelihood and whittle estimation procedures using a simulation study. Communications in Statistics: Simulation and Computation, 37(3), 560-570.
  • Thavaneswaran, A., Peiris, M., Appadoo, S. (2008). Random coefficient volatility models. Statistics and Probability Letters, 78, 582-593.
  • Perera, D., Peiris, M., Robinson, J., Weber, N. (2008). The empirical saddlepoint method applied to testing for serial correlation in panel time series data. Statistics and Probability Letters, 78(17), 2876-2882.
  • Peiris, M., Ng, K., Mohamed, I. (2007). A review of recent developments of financial time series: ACD modelling using the estimating function approach. Sri Lankan Journal of Applied Statistics, 8(1), 1-17.
  • Bertram, W., Peiris, M. (2007). An example of a classification problem applied to Australian equity data. Computational Statistics and Data Analysis, 51(8), 3627-3630. [More Information]
  • Peiris, M., Thavaneswaran, A. (2007). An introduction to volatility models with indices. Applied Mathematics Letters, 20(2), 177-182. [More Information]
  • Perera, D., Peiris, M., Robinson, J., Weber, N. (2006). Saddlepoint approximation methods for testing of serial correlation in panel time series data. Journal of Statistical Computation and Simulation, 76(11), 1001-1015.
  • Allen, D., Peiris, M., Yang, J. (2005). An examination of the role of time and its impact on price revision. Australian Journal of Management, 30(2), 283-301.
  • Thavaneswaran, A., Appadoo, S., Peiris, M. (2005). Forecasting volatility. Statistics and Probability Letters, 75(1), 1-10.
  • Peiris, M., Allen, D., Yang, W. (2005). Some statistical models for durations and an application to News Corporation stock prices. Mathematics and Computers in Simulation, 68(05-Jun), 549-556.
  • Peiris, M., Rao, C. (2004). A Note On Testing For Serial Correlation In Large Number Of Small Samples Using Tail Probability Approximations. Communications in Statistics - Theory and Methods, 33(8), 1767-1777.
  • Peiris, M., Thavaneswaran, A. (2004). A Note On The Filtering For Some Time Series Models. Journal of Time Series Analysis, 25(3), 397-407.
  • Peiris, M., Allen, D., Thavaneswaran, A. (2004). An Introduction to Generalized Moving Average Models and Applications. Journal of Applied Statistical Science, 13(3), 251-267.
  • Thavaneswaran, A., Peiris, M. (2004). Smoothed Estimates For Models With Random Coefficients And Infinite Variance Innovations. Mathematical and Computer Modelling, 39(4-5), 363-372.
  • Thavaneswaran, A., Peiris, M. (2003). Generalized smoothed estimating functions for nonlinear time series. Statistics and Probability Letters, 65(1), 51-56.
  • Peiris, M., Mellor, R., Ainkaran, P. (2003). Maximum likelihood estimation for short time series with replicated observations: a simulation study. InterStat, 11, 38719-42401.
  • Hunt, R., Peiris, M., Weber, N. (2003). The bias of lag window estimators of the fractional difference parameter. Journal of Applied Mathematics and Computing, 12(1-2), 67-79.
  • Peiris, M., Singh, N., Yadavalli, V. (2002). A note on the modelling and analysis of vector Arma processes with nonstationary innovations. Mathematical and Computer Modelling, 36(11-13), 1409-1424.
  • Peiris, M., Thavaneswaran, A. (2001). Inference for some time series models with random coefficients and infinite variance innovations. Mathematical and Computer Modelling, 33, 843-849.
  • Peiris, M., Thavaneswaran, A. (2001). Multivariate stable ARMA processes with time dependent coefficients. Metrika: international journal for theoretical and applied statistics, 54, 131-138.
  • Peiris, M., Thavaneswaran, A. (2001). On the properties of some nonstationary arma processes with infinite variance. International Journal Of Modelling And Simulation, 21, No. 4, 301-304.
  • Peiris, M., Thavaneswaran, A. (2001). Recursive estimation for regression with infinite variance fractional ARIMA noise. Mathematical and Computer Modelling, 34, 1133-1137.

Conferences

  • Dissanayake, G., Peiris, M., Proietti, T. (2014). Estimation of Generalized Fractionally Differenced Processes with Conditionally Heteroskedastic Errors. ITISE 2014 International Work Conference on Time Series Analysis, Granada, Spain: Copicentro Granada S L.
  • Ng, K., Peiris, M., Lai, S., Tiew, C. (2011). Efficient Estimation of Autoregressive Conditional Duration (ACD) Models using Estimating Functions (EF). The International Statistics Conference 2011: Statistical Concepts and Methods for the Modern World, Colombo, Sri Lanka: Institute of Applied Statistics, Sri Lanka.
  • Peiris, M. (2005). Generalised Autoregressive Models with Conditional Heteroscedasticity: An Application to Financial Time Series Modelling. The 2004 Workshop on Research Methods: Statistics and Finance, Wollongong: University of Wollongong.
  • Bertram, W., Peiris, M. (2005). Increasing the Quality of Volatility Forecasts with Fractional ARIMA Models. The 2004 Workshop on Research Methods: Statistics and Finance, Wollongong: University of Wollongong.
  • Peiris, M., Rao, C. (2004). An Application Of Edgeworth Expansion On Testing For Serial Correlation In Large Number Of Small Samples. The International Sri Lankan Statistical Conference: Visions of Futuristic Methodologies, Australia: RMIT.
  • Peiris, M., Peseta, T. (2004). Learning Statistics In First Year By Active Participating Students. Scholarly Inquiry into Science Teaching and Learning Symposium, Sydney, NSW: Uniserve Science.
  • Perera, D., Peiris, M. (2004). Significance Testing For Lag One Serial Correlation In Repeated Measurements Using Saddlepoint Approximation. The International Sri Lankan Statistical Conference: Visions of Futuristic Methodologies, Australia: RMIT.
  • Ainkaran, P., Peiris, M., Mellor, R. (2003). A note on the analysis of short AR(1) type time series models with replicated observations. Workshop on Advanced Research Methods, Australia: University of Western Sydney.
  • Perera, D., Peiris, M., Weber, N. (2003). A note on the distribution of serial correlation in large numbers of small samples. Advanced workshop on research methods, : National University of Singapore.

2014

  • Ng, K., Peiris, M., Gerlach, R. (2014). Estimation and forecasting with logarithmic autoregressive conditional duration models: A comparative study with an application. Expert Systems with Applications, 41(7), 3323-3332. [More Information]
  • Dissanayake, G., Peiris, M., Proietti, T. (2014). Estimation of Generalized Fractionally Differenced Processes with Conditionally Heteroskedastic Errors. ITISE 2014 International Work Conference on Time Series Analysis, Granada, Spain: Copicentro Granada S L.
  • Rosadi, D., Peiris, M. (2014). Second-order least-squares estimation for regression models with autocorrelated errors. Computational Statistics, 29(5), 931-943. [More Information]
  • Peiris, M. (2014). Testing the null hypothesis of zero serial correlation in short panel time series: a comparison of tail probabilities. Statistical Papers, 55(2), 513-523. [More Information]

2013

  • Shitan, M., Peiris, M. (2013). Approximate Asymptotic Variance-Covariance Matrix for the Whittle Estimators of GAR(1) Parameters. Communications in Statistics - Theory and Methods, 42(5), 756-770. [More Information]
  • Peiris, M. (2013). Efficient Estimation of Regression Models with Heteroscedastic Errors. Mathematical Scientist, 38, 124-128.
  • Allen, D., Ng, K., Peiris, M. (2013). Estimating and simulating Weibull models of risk or price durations: An application to ACD models. The North American Journal of Economics and Finance, 25, 214-225. [More Information]
  • Rosner, B., Peiris, M., Chan, J., Marchev, D. (2013). MATH1015: Biostatistics. Sydney: Cengage Learning.
  • Ng, K., Peiris, M. (2013). Modelling High Frequency Transaction Data in Financial Economics: A Comparative Study Based on Simulations. Journal of Economic Computation and Economic Cybernetics Studies and Research, 47(2), 189-201.
  • Allen, D., Ng, K., Peiris, M. (2013). The efficient modelling of high frequency transaction data: A new application of estimating functions in financial economics. Economics Letters, 120, 117-122. [More Information]

2012

  • Pillai, T., Shitan, M., Peiris, M. (2012). Some Properties of the Generalized Autoregressive Moving Average (GARMA (1, 1; 1, 2)) Model. Communications in Statistics - Theory and Methods, 41(4), 699-716. [More Information]

2011

  • Abdullah, N., Mohammed, I., Peiris, M., Azizan, A. (2011). A New Iterative Procedure for Estimation of RCA Parameters Based on Estimating Functions. Applied Mathematical Sciences, 5(4), 193-202.
  • Peiris, M., Thavaneswaran, A., Appadoo, S. (2011). Doubly stochastic models with GARCH innovations. Applied Mathematics Letters, 24(11), 1768-1773. [More Information]
  • Ng, K., Peiris, M., Lai, S., Tiew, C. (2011). Efficient Estimation of Autoregressive Conditional Duration (ACD) Models using Estimating Functions (EF). The International Statistics Conference 2011: Statistical Concepts and Methods for the Modern World, Colombo, Sri Lanka: Institute of Applied Statistics, Sri Lanka.
  • Dissanayake, G., Peiris, M. (2011). Generalized Fractional Processes with Conditional Heteroscedasticity. Sri Lankan Journal of Applied Statistics, 12(Special Issue 2011), 1-12.
  • Shitan, M., Peiris, M. (2011). Time Series Properties of the Class of Generalized First-Order Autoregressive Processes with Moving Average Errors. Communications in Statistics - Theory and Methods, 40(13), 2259-2275. [More Information]

2009

  • Shitan, M., Peiris, M. (2009). A Note on the properties of generalised separable spatial autoregressive process. Journal of Probability and Statistics, 2009, 847830-1-847830-11.
  • Allen, D., Lazarov, Z., McAleer, M., Peiris, M. (2009). Comparison of Alternative ACD Models via density and interval forecasts: Evidence from the Australian Stock Market. Mathematics and Computers in Simulation, 79(8), 2535-2555.
  • Pathmanathan, N., Ng, K., Peiris, M. (2009). On Estimation of Autoregressive Conditional Duration (ACD) Models Based on Different Error Distributions. Sri Lankan Journal of Applied Statistics, 10, 251-269.
  • Shitan, M., Peiris, M. (2009). On properties of the second order generalized autoregressive GAR(2) model with index. Mathematics and Computers in Simulation, 80(2), 367-377.
  • Pillai, T., Shitan, M., Peiris, M. (2009). Time series properties of the class of first order autoregressive processes with generalized moving average errors. Journal of Statistics: Advances in theory and applications, 2(1), 71-92.

2008

  • Thavaneswaran, A., Peiris, M., Singh, J. (2008). Derivation of Kurtosis and option price formulae for popular volatility models with applications in finance. Communications in Statistics - Theory and Methods, 37(11), 1799-1814.
  • Allen, D., Chan, F., McAleer, M., Peiris, M. (2008). Finite sample properties of the QMLE for the Log-ACD model: Application to Australian stocks. Journal of Econometrics, 147(1), 163-185. [More Information]
  • Shitan, M., Peiris, M. (2008). Generalized autoregressive (GAR) model: A comparison of maximum likelihood and whittle estimation procedures using a simulation study. Communications in Statistics: Simulation and Computation, 37(3), 560-570.
  • Thavaneswaran, A., Peiris, M., Appadoo, S. (2008). Random coefficient volatility models. Statistics and Probability Letters, 78, 582-593.
  • Perera, D., Peiris, M., Robinson, J., Weber, N. (2008). The empirical saddlepoint method applied to testing for serial correlation in panel time series data. Statistics and Probability Letters, 78(17), 2876-2882.

2007

  • Peiris, M., Ng, K., Mohamed, I. (2007). A review of recent developments of financial time series: ACD modelling using the estimating function approach. Sri Lankan Journal of Applied Statistics, 8(1), 1-17.
  • Bertram, W., Peiris, M. (2007). An example of a classification problem applied to Australian equity data. Computational Statistics and Data Analysis, 51(8), 3627-3630. [More Information]
  • Peiris, M., Thavaneswaran, A. (2007). An introduction to volatility models with indices. Applied Mathematics Letters, 20(2), 177-182. [More Information]

2006

  • Perera, D., Peiris, M., Robinson, J., Weber, N. (2006). Saddlepoint approximation methods for testing of serial correlation in panel time series data. Journal of Statistical Computation and Simulation, 76(11), 1001-1015.

2005

  • Allen, D., Peiris, M., Yang, J. (2005). An examination of the role of time and its impact on price revision. Australian Journal of Management, 30(2), 283-301.
  • Thavaneswaran, A., Appadoo, S., Peiris, M. (2005). Forecasting volatility. Statistics and Probability Letters, 75(1), 1-10.
  • Peiris, M. (2005). Generalised Autoregressive Models with Conditional Heteroscedasticity: An Application to Financial Time Series Modelling. The 2004 Workshop on Research Methods: Statistics and Finance, Wollongong: University of Wollongong.
  • Bertram, W., Peiris, M. (2005). Increasing the Quality of Volatility Forecasts with Fractional ARIMA Models. The 2004 Workshop on Research Methods: Statistics and Finance, Wollongong: University of Wollongong.
  • Peiris, M., Allen, D., Yang, W. (2005). Some statistical models for durations and an application to News Corporation stock prices. Mathematics and Computers in Simulation, 68(05-Jun), 549-556.

2004

  • Peiris, M., Rao, C. (2004). A Note On Testing For Serial Correlation In Large Number Of Small Samples Using Tail Probability Approximations. Communications in Statistics - Theory and Methods, 33(8), 1767-1777.
  • Peiris, M., Thavaneswaran, A. (2004). A Note On The Filtering For Some Time Series Models. Journal of Time Series Analysis, 25(3), 397-407.
  • Peiris, M., Rao, C. (2004). An Application Of Edgeworth Expansion On Testing For Serial Correlation In Large Number Of Small Samples. The International Sri Lankan Statistical Conference: Visions of Futuristic Methodologies, Australia: RMIT.
  • Peiris, M., Allen, D., Thavaneswaran, A. (2004). An Introduction to Generalized Moving Average Models and Applications. Journal of Applied Statistical Science, 13(3), 251-267.
  • Peiris, M., Peseta, T. (2004). Learning Statistics In First Year By Active Participating Students. Scholarly Inquiry into Science Teaching and Learning Symposium, Sydney, NSW: Uniserve Science.
  • Perera, D., Peiris, M. (2004). Significance Testing For Lag One Serial Correlation In Repeated Measurements Using Saddlepoint Approximation. The International Sri Lankan Statistical Conference: Visions of Futuristic Methodologies, Australia: RMIT.
  • Thavaneswaran, A., Peiris, M. (2004). Smoothed Estimates For Models With Random Coefficients And Infinite Variance Innovations. Mathematical and Computer Modelling, 39(4-5), 363-372.

2003

  • Ainkaran, P., Peiris, M., Mellor, R. (2003). A note on the analysis of short AR(1) type time series models with replicated observations. Workshop on Advanced Research Methods, Australia: University of Western Sydney.
  • Perera, D., Peiris, M., Weber, N. (2003). A note on the distribution of serial correlation in large numbers of small samples. Advanced workshop on research methods, : National University of Singapore.
  • Thavaneswaran, A., Peiris, M. (2003). Generalized smoothed estimating functions for nonlinear time series. Statistics and Probability Letters, 65(1), 51-56.
  • Peiris, M., Mellor, R., Ainkaran, P. (2003). Maximum likelihood estimation for short time series with replicated observations: a simulation study. InterStat, 11, 38719-42401.
  • Hunt, R., Peiris, M., Weber, N. (2003). The bias of lag window estimators of the fractional difference parameter. Journal of Applied Mathematics and Computing, 12(1-2), 67-79.

2002

  • Peiris, M., Singh, N., Yadavalli, V. (2002). A note on the modelling and analysis of vector Arma processes with nonstationary innovations. Mathematical and Computer Modelling, 36(11-13), 1409-1424.

2001

  • Peiris, M., Thavaneswaran, A. (2001). Inference for some time series models with random coefficients and infinite variance innovations. Mathematical and Computer Modelling, 33, 843-849.
  • Peiris, M., Thavaneswaran, A. (2001). Multivariate stable ARMA processes with time dependent coefficients. Metrika: international journal for theoretical and applied statistics, 54, 131-138.
  • Peiris, M., Thavaneswaran, A. (2001). On the properties of some nonstationary arma processes with infinite variance. International Journal Of Modelling And Simulation, 21, No. 4, 301-304.
  • Peiris, M., Thavaneswaran, A. (2001). Recursive estimation for regression with infinite variance fractional ARIMA noise. Mathematical and Computer Modelling, 34, 1133-1137.

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