Publications for Shelton Peiris
Publications for Shelton Peiris
Granada: Copicentro Granada S L.
2016
Rosadi, D., Peiris, M. (2014). Second-order
least-squares estimation for regression
Gerlach, R., Peiris, M., Lin, E. (2016). Bayesian
estimation and inference for log-ACD models.
Computational Statistics, 31(1), 25-48. <a
href="http://dx.doi.org/10.1007/s00180-015-057
6-">[More Information]</a>
models with autocorrelated errors.
Computational Statistics, 29(5), 931-943. <a
href="http://dx.doi.org/10.1007/s00180-013-047
0-1">[More Information]</a>
Allen, D., McAleer, M., Peiris, M., Singh, A.
(2016). Nonlinear Time Series and
Neural-Network Models
of Exchange Rates between the US Dollar and
Major Currencies. Risks, 4(7), 1-14. <a
href="http://dx.doi.org/10.3390/risks4010007">[
More Information]</a>
Dissanayake, G., Peiris, M., Proietti, T. (2016).
State space modeling of Gegenbauer processes
with
long memory. Computational Statistics and Data
Analysis, 100, 115-130. <a
href="http://dx.doi.org/10.1016/j.csda.2014.09.0
14">[More Information]</a>
Dissanayake, G., Peiris, M., Proietti, T. (2016).
State space modeling of Gegenbauer processes
with long memory. Computational Statistics and
Data Analysis, 100, 115-130. <a
href="http://dx.doi.org/10.1016/j.csda.2014.09.0
14">[More Information]</a>
2015
Ong, S., Biswas, A., Peiris, M., Low, Y. (2015).
Count Distribution for Generalized Weibull
Duration with Applications. Communications in
Statistics - Theory and Methods, 44(19),
4203-4216. <a
href="http://dx.doi.org/10.1080/03610926.2015.
1062105">[More Information]</a>
Ng, K., Peiris, M., Thavaneswaran, A., Ng, K.
(2015). Modelling the risk or price durations in
financial markets: quadratic estimating functions
and applications. Economic Computation and
Economic Cybernetics Studies and Research,
49(1), 223-237.
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. <a
href="http://dx.doi.org/10.1016/j.eswa.2013.11.0
24">[More Information]</a>
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,
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. <a
href="http://dx.doi.org/10.1007/s00362-012-049
5-5">[More Information]</a>
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.
<a
href="http://dx.doi.org/10.1080/03610926.2011.
569862">[More Information]</a>
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. <a
href="http://dx.doi.org/10.1016/j.najef.2012.06.0
13">[More Information]</a>
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. <a
href="http://dx.doi.org/10.1016/j.econlet.2013.03
.049">[More Information]</a>
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. <a
Publications for Shelton Peiris
href="http://dx.doi.org/10.1080/03610926.2010.
529534">[More Information]</a>
href="http://dx.doi.org/10.1016/j.matcom.2009.0
7.007">[More Information]</a>
2011
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.
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. <a
href="http://dx.doi.org/10.1016/j.aml.2011.04.02
0">[More Information]</a>
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. <a
href="http://dx.doi.org/10.1080/0361092100376
5784">[More Information]</a>
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. <a
href="http://dx.doi.org/10.1016/j.matcom.2008.1
2.014">[More Information]</a>
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. <a
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. <a
href="http://dx.doi.org/10.1016/j.jeconom.2008.
09.020">[More Information]</a>
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. <a
href="http://dx.doi.org/10.1080/0361091070164
9598">[More Information]</a>
Thavaneswaran, A., Peiris, M., Appadoo, S.
(2008). Random coefficient volatility models.
Statistics and Probability Letters, 78, 582-593.
<a
href="http://dx.doi.org/10.1016/j.spl.2007.09.019
">[More Information]</a>
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. <a
href="http://dx.doi.org/10.1016/j.spl.2008.04.010
">[More Information]</a>
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. <a
href="http://dx.doi.org/10.1016/j.csda.2006.12.0
07">[More Information]</a>
Peiris, M., Thavaneswaran, A. (2007). An
introduction to volatility models with indices.
Publications for Shelton Peiris
Applied Mathematics Letters, 20(2), 177-182. <a
href="http://dx.doi.org/10.1016/j.aml.2006.04.00
1">[More Information]</a>
Scholarly Inquiry into Science Teaching and
Learning Symposium, Sydney, NSW: Uniserve
Science.
2006
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.
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.
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.
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.
Thavaneswaran, A., Peiris, M. (2003).
Generalized smoothed estimating functions for
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., Mellor, R., Ainkaran, P. (2003).
Maximum likelihood estimation for short time
series with replicated observations: a simulation
study. InterStat, 11, 38719-42401.
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.
nonlinear time series. Statistics and Probability
Letters, 65(1), 51-56.
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
Publications for Shelton Peiris
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.