Publication Search Results
Matches for:
- Author = Peiris MS
1.
Gadhi AHA, Peiris MS, Allen DE
Adel Hassan A Gadhi, Shelton Peiris and David E Allen:
Improving Volatility Forecasting: A Study through Hybrid Deep Learning Methods with WGAN,
Journal of Risk and Financial Management,
17
(2024),
Article 38 (20 Pages).
2.
Hunt RL, Peiris MS, Weber NC
Richard Hunt, Shelton Peiris and Neville Weber:
Seasonal generalized AR models,
Communications in Statistics - Theory and Methods,
53
(2024),
no. 3,
1065–1080. Published online on 21 July 2022.
3.
Jajo N, Peiris MS
Nethal Jajo and Shelton Peiris:
Python and R in Statistics and Data Science,
Lambert Academic Publishing,
London/Republic of Moldowa,
(2023),
40.
ISBN 978-620-6-84564-5.
4.
Allen DE, Peiris MS
David Edmund Allen and Shelton Peiris:
GARMA, HAR and Rules of Thumb for Modelling Realized Volatility,
Risks,
11
(2023),
no. 179,
15 pages.
5.
Allen DE, Mushunje L, Peiris MS
D E Allen, L Mushunje and S. Peiris:
GANs through the looking glass: How real is the fake financial data created by Generative Adversarial Neural Nets?,
Proceedings of the 25th International Congress on Modelling and Simulation,
The 25th International Congress on Modelling and Simulation (MODSIM2023),
Vaze J, Chilcott C., Hutley L and Cuddy S M (eds.),
Modelling and Simulation Society of Australia and New Zealand Inc. © 2023,
Australia,
(2023),
29–35.
ISBN 978-0-9872143-0-0.
6.
Hunt RL, Peiris MS, Weber NC
Richard Hunt, Shelton Peiris and Nevlle Weber:
Bayesian estimation of Gegenbauer processes,
Journal of Statistical Computation and Simulation,
93
(2023),
no. 9,
1357–1377. Published online on 6 November 2022.
7.
Peiris MS, Hunt RL
Shelton Peiris, Richard Hunt:
Revisiting the Autocorrelation of Long Memory Time Series Models,
Mathematics,
11 (Gold Open Access)
(2023),
no. 4,
Article 817 (8 pages).
8.
Zhou JJ, Ng KH, Ng KH, Peiris MS, Koh YB
Jing Jia Zhou, Kok Haur Ng, Kooi Huat Ng, Shelton Peiris and You Beng Koh:
Asymmetric Control Limits for Weighted-Variance Mean Control Chart with Different Scale Estimators under Weibull Distributed Process,
Mathematics,
10
(2022),
no. 22,
Article 4380 (15 pages).
9.
Tan YF, Ng KH, Koh YB, Peiris MS
Yiing Fei Tan, Kok Haur Ng, You Beng Koh, Shelton Peiris:
Modelling Trade Durations Using Dynamic Logarithmic Component ACD Model with Extended Generalised Inverse Gaussian Distribution,
Mathematics,
10
(2022),
no. 1621,
1–20.
10.
Tan YF, Ng KH, Koh YB, Peiris MS
Yiing Fei Tan, Kok Haur Ng, You Beng Koh and Shelton Peiris:
Modelling Trade Durations Using Dynamic Logarithmic Component ACD Model with Extended Generalised Inverse Gaussian Distribution,
Mathematics,
10
(2022),
no. 10 (Open Access),
Article 1026 (20 pages).
11.
Alhuntushi NMW, Jajo NK, Peiris MS, Khadra M, Mallows J
Nasser Mutlaq W Alhuntushi, Nethal K Jajo, Shelton Peiris, Mohamed Khadra, James Mallows:
A New Look at Patient Waiting Time in an Australian Emergency Department using Simulation,
International Journal of Statistics and Systems,
17
(2022),
no. 1,
1–18.
12.
Hunt RL, Peiris MS, Weber NC
Richard Hunt, Shelton Peiris, Neville Weber:
Estimation methods for stationary Gegenbauer processes,
Statistical Papers,
63
(2022),
1707–1741.
13.
Fang Z, Dowe DL, Peiris MS, Rosadi D
Zheng Fang, David L. Dowe, Shelton Peiris, Dedi Rosadi:
Minimum Message Length in Hybrid ARMA and LSTM Model Forecasting,
Entropy,
23
(2021),
1–21.
14.
Jajo NK, Peiris MS
Nethal K. Jajo and Shelton Peiris:
A Study on Efficient Modelling in Higher Education Academic Workforce Using Simulation,
EJ-MATH, European Journal of Mathematics and Statistics,
Vol2
(2021),
no. 6,
7–14.
15.
Hunt RL, Peiris MS, Weber NC
Richard Hunt, Shelton Peiris and Neville Weber:
A general frequency domain estimation method for Gegenbauer processes,
Journal of Time Series Econometrics,
13
(2021),
no. 2,
119–144.
16.
Peiris MS, Chan JSK, Jajo NK
Shelton Peiris, Jennifer Chan, Nethal Jajo:
A Quick Reference Guide to Beginners of Statistics and Data Science Using RStudio,
CV. Meugah Printindo,
Indonesia,
(2021),
276.
ISBN 978-623-97079-0-3.
17.
Phillip A, Chan JSK, Peiris MS
Andrew Phillip, Jennifer Chan, Shelton Peiris:
On generalized bivariate student-t Gegenbauer long memory stochastic volatility models with leverage: Bayesian forecasting of cryptocurrencies with a focus on Bitcoin,
Econometrics and Statistics,
16
(2020),
69–90.
18.
Asai M, McAleer M, Peiris MS
Manabu Asai Michael McAleer Shelton Peiris:
Realized stochastic volatility models with generalized Gegenbauer long memory,
Econometrics and Statistics,
16
(2020),
42–54.
19.
Yatigammana R, Peiris MS, Gerlach R, Allen DE
Rasika Yatigammana, Shelton Peiris, Richard Gerlach and David Edmund Allen:
Modelling and Forecasting Stock Price Movements with Serially Dependent Determinants,
Computational Methods for Risk Management in Economics and Finance,
Risks,
MDPI,
Switzerland,
(2020),
22 pages.
ISBN 978-3-03928-499-3.
20.
Leong XY, Jajo NK, Peiris MS
Xing Yee Leong, Nethal K. Jajo, Shelton Peiris:
Discrete Simulation on Elective Surgery Wait Line Using Arena Simulation Software,
International Journal of Modelling and Optimization,
10
(2020),
no. 2,
47–51.
21.
Asai M, Peiris MS, McAleer M, Allen DE
Manabu Asai, Shelton Peiris, Michael McAleer, David E. Allen:
Cointegrated Dynamics for a Generalized Long Memory Process: Application to Interest Rates,
Journal of Time Series Econometrics.,
12
(2020),
1–18.
22.
Peiris MS, Swartz T
Shelton Peiris and Tim Swartz:
Revisiting the Kurtosis of Stationary Processes with Applications to Volatility Models,
Journal of Statistical and Econometric Methods,
9
(2020),
no. 2,
1–17.
23.
Peiris MS, Swartz T
Shelton Peiris and Tim Swartz:
Developments and Applications of Biostatistical Time Series: A Review,
Annals of Biostatistics & Biometric Applications,
Volume 3
(2019),
no. Issue 5,
1–4.
24.
Chan JSK, Ng KH, Nitithumbundit T, Peiris MS
Jennifer So Kuen Chan, Kok-Haur Ng, Thanakorn Nitithumbundit, Shelton Peiris:
Efficient estimation of financial risk by regressing the quantiles of parametric distributions: An application to CARR models,
Studies in Nonlinear Dynamics & Econometrics,
23
(2019),
no. 2,
1–22.
MR3948450
25.
Phillip A, Chan JSK, Peiris MS
Andrew Phillip, Jennifer Chan, Shelton Peiris:
On long memory effects in the volatility measure of cryptocurrencies,
Finance Research Letters,
28
(2019),
95–100.
26.
Allen DE, Kalev P, Peiris MS, Singh AK
David E Allen, Petko Kalev, Shelton Peiris and Abhay K Singh:
Currency Spillover Effects between the US Dollar and Some Major Currencies and Exchange Rate Forecasts Based on Neural Nets,
Handbook of Global Financial Markets; Transformations, Dependence, and Risk Spillovers,
World Scientific Publishing Co.,
Singapore,
(2019),
197–218.
ISBN 978-981-3236-64-6.
27.
Phillip A, Chan JSK, Peiris MS
Andrew Phillip, Chan Jennifer S.K., Peiris Shelton:
Bayesian estimation of Gegenbauer long memory processes with stochastic volatility: methods and applications,
Studies in Nonlinear Dynamics & Econometrics,
22
(2018),
no. 3,
1–29.
28.
Wu H, Peiris MS
Hao Wu and Shelton Peiris:
An introduction to vector Gegenbauer processes with long memory,
Stat,
7
(2018),
no. July 2018,
e197 – 20 pages.
29.
Dissanayake GS, Peiris MS, Proietti T
G. S. Dissanayake, M. S. Peiris and T. Proietti:
Fractionally differenced Gegenbauer processes with long memory: a review,
Statistical Science,
33
(2018),
no. 3,
413–426.
30.
Yatigammana R, Peiris MS, Gerlach R, Allen DE
Rasika Yatigammana, Shelton Peiris, Richard Gerlach and David Edmund Allen:
Modelling and Forecasting Stock Price Movements with Serially Dependent Determinants,
Risks,
6
(2018),
no. 52,
22 pages.
31.
Phillip A, Chan JSK, Peiris MS
Andrew Phillip, Jennifer S.K.Chan, Shelton Peiris:
A new look at Cryptocurrencies,
Economics Letters,
163
(2018),
6–9.
32.
Peiris MS, Asai M, McAleer M
Shelton Peiris , Manabu Asai, Michael McAleer:
Estimating and Forecasting with Generalized Fractional Long Memory Stochastic Volatility Models,
Journal of Risk and Financial Management,
10
(2017),
no. 23,
16.
33.
Ng KH, Peiris MS, Chan JSK, Allen DE, Ng KH
Kok Haur Ng, Shelton Peiris, Jennifer So-kuen Chan, David Allen, Kooi Huat Ng:
Efficient modelling and forecasting with range based volatility models and its application,
North American Journal of Economics and Finance,
42
(2017),
448–460.
34.
Peiris MS, Asai M
M Shelton Peiris and Manabu Asai:
Generalized Fractional Processes with Long Memory and Time Dependent Volatility Revisited,
Econometrics,
4 (3)
(2016),
no. 37,
21 pages.
35.
Dissanayake GS, Peiris MS, Proietti T
G S Dissanayake, M S Peiris, T Proietti:
State space modeling of Gegenbauer processes with long memory,
Computational Statistics and Data Analysis,
100
(2016),
115–130.
MR3505794
36.
Gerlach R, Peiris MS, Lin EMH
Richard Gerlach, Shelton Peiris, Edward M H Lin:
Bayesian estimation and inference for log-ACD models,
Computational Statistics,
31
(2016),
no. 1,
25–48.
MR3481795
37.
Allen DE, McAleer M, Peiris MS, Singh AK
David E Allen, Michael McAleer, Shelton Peiris and Abhay K Singh:
Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies,
Risks,
4
(2016),
no. 7,
14 pages.
38.
Ong SH, Biswas A, Peiris MS, Low YC
S H Ong, Atanu Biswas, S Peiris and Y C Low:
Count Distribution for Generalized Weibull Duration with Applications,
Communications in Statistics - Theory and Methods,
44
(2015),
no. 19,
4203–4216.
39.
Ng KH, Peiris MS, Thavaneswaran A, Ng KH
Koh-Haur Ng, Shelton Peiris, Aerambamoorthy Thavaneswaran, Kooi-Huat Ng:
Modelling the risk or price durations in financial markets: quadratic estimating functions and applications,
Economic computation and economic cybernetics studies and research,
49
(2015),
no. 1,
223–238.
40.
Rosadi D, Peiris MS
Dedi Rosadi and Shelton Peiris:
Second-order least-squares estimation for regression models with autocorrelated errors,
Computational Statistics,
29
(2014),
no. 5,
931–943.
41.
Dissanayake GS, Peiris MS, Proietti T
G S Dissanayake, M S Peiris and T Proietti:
Estimation of Generalized Fractionally Differenced Processes with Conditionally Heteroskedastic Errors,
Proceedings ITISE 2014,
International Work Conference on Time Series,
Ignacio Rojas Ruiz and Gonzalo Ruiz Garcia (eds.),
Copicentro Granada S L,
Granada,
(2014),
871–890.
ISBN 978-84-15814-97-9.
42.
Peiris MS
Shelton Peiris:
Testing the null hypothesis of zero serial correlation in short panel time series: a comparison of tail probabilities,
Statistical Papers,
55
(2014),
no. 2,
513–523.
43.
Ng KH, Peiris MS, Gerlach R
K.H. Ng, Shelton Peiris, Richard Gerlach:
Estimation and forecasting with logarithmic autoregressive conditional duration models: A comparative study with an application,
Expert Systems with Applications,
41
(2014),
no. 7,
3323–3332.
44.
Peiris MS
M.S.Peiris:
Efficient Estimation of Regression Models with Heteroscedastic Errors,
Mathematical Scientist,
38
(2013),
124–128.
45.
Ng KH, Peiris MS
Ng, Kok-Haur and Peiris, S.:
Modelling High Frequency Transaction Data in Financial Economics: A Comparative Study Based on Simulations,
Journal of Economic Computation and Economic Cybernetics Studies and Research (ECECSR),
47
(2013),
no. 2,
189–202.
46.
Allen DE, Ng KH, Peiris MS
David Allen , K.H. Ng , Shelton Peiris:
Estimating and simulating Weibull models of risk or price durations: An application to ACD models,
North American Journal of Economics and Finance,
25
(2013),
214–225.
47.
Allen DE, Ng KH, Peiris MS
David Allen, K.H. Ng , Shelton Peiris:
The efficient modelling of high frequency transaction data: A new application of estimating functions in financial economics,
Economics Letters,
120
(2013),
117–122.
48.
Rosner B, Peiris MS, Chan JSK, Marchev D
Rosner, B., Peiris, S., Chan, J., Marchev, D.:
MATH1015: Biostatistics,
Third Edition,
Cengage Learning,
Australia,
(2013),
296.
ISBN 978-0170257916.
49.
Shitan M, Peiris MS
Mahendran Shitan and Shelton Peiris:
Approximate Asymptotic Variance-Covariance Matrix for the Whittle Estimators of GAR(1) Parameters,
Communications in Statistics---Theory and Methods,
42
(2013),
no. 5,
756–770.
50.
Dissanayake GS, Peiris MS
Gnanadarsha Dissanayake, Shelton Peiris:
Generalized Fractional Processes with Conditional Heteroskedasticity,
Sri Lankan Journal of Applied Statistics,
12 (2011)
(2012),
1–12.
51.
Pillai TR, Shitan M, Peiris MS
Thulasyammal R. Pillai, Mahendran Shitan and Shelton Peiris:
Some Properties of the Generalized Autoregressive Moving Average (GARMA \((1, 1; \delta_1, \delta_2)\)) Model,
Communications in Statistics -- Theory and Methods,
41
(2012),
699–716.
52.
Ng KH, Peiris MS, Lai SY, Tiew CS
K.H.Ng, S.Peiris, S.Y.Lai, C.S.Tiew:
Efficient Estimation of ACD Models Using Estimating Functions,
Proceedings of the International Statistics Conference 2011: Statistical Concepts and Methods for the Modern World,
Statistical Concepts and Methods for the Modern World,
S.Peiris, S.G.Banneheka, C.D.Tilakaratne, T.B.Swartz, S. Ganesalingam (eds.),
Institute of Applied Statistics, Sri Lanka,
Colombo, Sri Lanka,
(2011),
122–134.
ISBN 978-955-0056-01-9.
53.
Peiris MS, Thavaneswaran A, Appadoo S
S. Peiris, A. Thavaneswaran, S. Appadoo:
Doubly stochastic models with GARCH innovations,
Applied Mathematics Letters,
24
(2011),
no. 11,
1768–1773.
54.
Shitan M, Peiris MS
Mahendran Shitan and Shelton Peiris:
Time Series Properties of the Class of Generalized First-Order Autoregressive Processes with Moving Average Errors,
Communications in Statistics - Theory and Methods,
40
(2011),
no. 13,
2259–2275.
55.
Rosner B, Peiris MS, Chan JSK, Marchev D
Bernard Rosner, Shelton Peiris, Jennifer Chan, Dobrin Marchev:
Descriptive Statistics,
MATH 1015: Biostatistics,
CENGAGE Learning,
Australia,
(2011),
272.
ISBN 978-0170213349.
56.
Abdulla NA, Mohamed I, Peiris MS, Azizan NA
Norli Anida Abdullah, Ibrahim Mohamed, Shelton Peiris and Nor Azlinna Azizan:
A New Iterative Procedure for Estimation of RCA Parameters based on Estimating Functions,
Applied Mathematical Sciences,
Vol. 5
(2011),
no. No 4,
193 – 202,.
57.
Pathmanathan D, Ng KH, Peiris MS
D.Pathmanathan, K.H.Ng, S.Peiris:
On Estimation of ACD Models with Different Error Distributions,
Sri Lankan Journal of Applied Statistics,
10
(2009),
251–269.
58.
Pillai TR, Shitan M, Peiris MS
T.Ramiah Pillai, M.Shitan, S.Peiris:
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
(2009),
no. 1,
71–92.
59.
Shitan M, Peiris MS
Shitan, M. and Peiris, S.:
On properties of the second order generalized autoregressive GAR(2) model with index,
Mathematics and Computers in Simulation,
80
(2009),
no. Issue 2,
367–377.
MR2582119
60.
Shitan M, Peiris MS
Shitan, Mahendran and Peiris, Shelton:
Note on the Properties of Generalised Separable Spatial Autoregressive Process,
Journal of Probability and Statistics,
vol. 2009
(2009),
1–11.
61.
Allen DE, Lazarov L, McAleer M, Peiris MS
D.E.Allen, L.Lazarov, M.McAleer, S.Peiris:
Comparison of Alternative ACD Models via density and interval forecasts: Evidence from the Australian Stock Market,
Mathematics and Computers in Simulation,
79
(2009),
no. 8,
2535–2555.
MR2531468
62.
Allen DE, Chan F, McAleer M, Peiris MS
D. Allen, F. Chan, M. McAleer and S. Peiris:
Finite sample properties of the QMLE for the Log-ACD model: Application to Australian stocks,
Journal of Econometrics,
147
(2008),
163–185.
MR2472990
63.
Perera DI, Peiris MS, Robinson J, Weber NC
DI Perera, MS Peiris, J Robinson, NC Weber:
The empirical saddlepoint method applied to testing for serial correlation in panel time series data,
Statistics and Probability Letters,
78
(2008),
2876–2882.
64.
Thavaneswaran A, Peiris MS, Singh J
A.Thavaneswaran, S.Peiris, J.Singh:
Derivation of Kurtosis and Option Pricing Formulae for Popular Volatility Models with Applications in Finance,
Communications in Statistics---Theory and Methods,
37
(2008),
no. 1,
1799–1814.
MR2431451
65.
Thavaneswaran A, Peiris MS, Appadoo S
Thavaneswaran, A., Peiris, S., Appadoo,S.:
Random Coefficient Volatility Models,
Statistics and Probability Letters,
78
(2008),
582–593.
MR2409521
66.
Shitan M, Peiris MS
Shitan, M. and Peiris, S.:
Generalized Autoregressive (GAR) Model: A Comparison of Maximum Likelihood and Whittle Estimation Procedures Using a Simulation Study,,
Communications in Statistics, Simulation and Computation,
37
(2008),
560–570..
67.
Peiris MS, Ng KH, Ibrahim IM
Peiris, M.S., Ng, K.H., Ibrahim, I.M.,:
A Review of Recent Developments of Financial Time Series: ACD Modelling using the Estimating Function Approach,
Sri Lankan Journal of Applied Statistics,
8
(2007),
1–17.
68.
Bertram WK, Peiris MS
William K Bertram, M Shelton Peiris:
An example of a misclassification problem to Australian equity data,
Computational Statistics and Data Analysis,
51
(2007),
3627–3630.
MR2364479
69.
Peiris MS, Thavaneswaran A
S Peiris, A Thavaneswaran:
An introduction to volatility models with indices,
Applied Mathematics Letters,
20
(2007),
no. 2,
177–182.
MR2283907
70.
Perera DI, Peiris MS, Robinson J, Weber NC
D I Perera, M S Peiris, J Robinson and N C Weber:
Saddlepoint approximation methods for testing of serial correlation in panel time series data,
Journal of Statistical Computation and Simulation,
76
(2006),
no. 11,
1001–1015.
MR2255899
71.
Allen DE, Peiris MS, Yang JW
David Allen, Shelton Peiris and Joey W Yang:
An examination of the role of time and its impact on price revision,
Australian Journal of Management,
30
(2005),
no. 2,
283–301.
72.
Peiris MS, Allen DE, Peiris U
Shelton Peiris, David Allen, Udara Peiris:
Generalised autoregressive models with conditional heteroscedasticity: An application to financial time series modelling,
Proceedings of the 2004 Workshop on Research Methods: Statistics and Finance,
The 2004 Workshop on Research Methods: Statistics and Finance,
Eric J Beh, Robert G Clark, J C W Rayner (eds.),
University of Wollongong,
Wollongong,
(2005),
75–83.
ISBN 1 74128 107 5.
73.
Bertram WK, Peiris MS
William K Bertram, M Shelton Peiris:
Increasing the quality of volatility forecasts with fractional ARIMA models,
Proceedings of the 2004 Workshop on Research Methods: Statistics and Finance,
The 2004 Workshop on Research Methods: Statistics and Finance,
Eric J Beh, Robert G Clark, J C W Rayner (eds.),
University of Wollongong,
Wollongong,
(2005),
66–74.
ISBN 1 74128 107 5.
74.
Thavaneswaran A, Appadoo S, Peiris MS
A. Thavaneswaran, S. Appadoo and S. Peiris:
Forecasting volatility,
Statistics and Probability Letters,
75
(2005),
1–10.
MR2185597
75.
Peiris MS, Allen DE, Yang W
Shelton Peiris, David Allen and Wenling Yang:
Some statistical models for durations and an application to News Corporation stock prices,
Mathematics and Computers in Simulation,
68
(2005),
549–556.
MR2156401
76.
Peiris MS, Allen DE, Thavaneswaran A
Shelton Peiris, David Allen and A. Thavaneswaran:
An introduction to generalized moving average model and applications,
Journal of Applied Statistical Science,
13
(2004),
no. 3,
251–267.
MR2162151
77.
Perera DI, Peiris MS
D. I. Perera and M. S. Peiris:
Significance testing for Lag One serial correlation in repeated measurements using saddlepoint approximation,
Sri Lankan Statistical Conference,
Visions of Futuristic Methodologies,
B. M. de Silva and N. Mukhopadhyay (eds.),
PGIS, University of Peradeniya,
Peradeniya, Sri Lanka,
(2004),
363–370.
ISBN 0 86459 339 2.
78.
Peiris MS, Rao CR
M. S. Peiris and C. R. Rao:
An application of Edgeworth expansion on testing for serial correlation in large number of small samples,
Sri Lankan Statistical Conference,
Visions of Futuristic Methodologies,
B. M. de Silva and N. Mukhopadhyay (eds.),
PGIS, University of Peradeniya,
Peradeniya, Sri Lanka,
(2004),
341–354.
ISBN 0 86459 339 2.
79.
Peiris MS, Rao CR
M. S. Peiris and C. R. Rao:
A note on testing for serial correlation in large number of small samples using tail probability approximations,
Communications in Statistics. Theory and Methods,
33
(2004),
no. 8,
1767–1777.
MR2065173
80.
Peiris MS, Thavaneswaran A
S. Peiris and A. Thavaneswaran:
A note on the filtering for some time series models,
Journal of Time Series Analysis,
25
(2004),
no. 3,
397–407.
MR2063642
81.
Thavaneswaran A, Peiris MS
A. Thavaneswaran and S. Peiris:
Smoothed estimates for models with random coefficients and infinite variance,
Mathematical Computation and Modelling,
39
(2004),
363–372.
MR2046529
82.
Peiris MS, Thavaneswaran A, Allen DE, Mellor R
M.S.Peiris, A.Thavaneswaran, D.Allen, R.Mellor:
Applications of recursive estimation methods in statistical process control: a comparison,
Statistical Methods,
5
(2003),
no. 2,
172–183.
MR2198742
83.
Peiris MS
M. Shelton Peiris:
Improving the quality of forecasting using generalized AR models: an application to statistical quality control,
Statistical Methods,
5
(2003),
no. 2,
156–171.
MR2198741
84.
Perera DI, Peiris MS, Weber NC
D. Perera, S. Peiris and N. Weber:
A Note on the Distribution of Serial Correlation in Large number of Small Samples,
Current Research in Modelling, Data Mining and Quantitative Techniques,
University of Western Sydney Press,
University of Western Sydney,
(2003),
172–192.
ISBN 0-975-1599-0-9.
85.
Ainkaran P, Peiris MS, Mellor R
P.Ainkaran, S.Peiris, R.Mellor:
A note on the analysis of short AR(1) type time series models with replicated observations,
Current Research in Modelling, Data Mining and Quantitative Techniques,
University of Western Sydney Press,
University of Western Sydney,
(2003),
143–156.
ISBN 0-975-1599-0-9.
86.
Pemajayantha V, Mellor R, Peiris MS, Rajasekera R
V.Pemajayantha, R.Mellor, S.Peiris, R.Rajasekera:
Current Research in Modelling, Data Mining and Quantitative Techniques,
University of Western Sydney,
University of Western Sydney Press,
(2003),
314.
ISBN 0-975-1599-0-9.
87.
Peiris MS, Mellor R, Ainkaran P
S. Peiris, R. Mellor and P. Ainkaran.:
Maximum likelihood estimation for short time series with replicated observations: a simulation study,
InterStat,
9, 11
(2003),
no. 3,
1–16.
88.
Thavaneswaran A, Peiris MS
A. Thavaneswaran and Shelton Peiris:
Generalized smoothed estimating functions for nonlinear time series,
Statistics and Probability Letters,
65
(2003),
51–56.
MR2012624
89.
Peiris MS, Rao CR
Peiris, M.S. and Rao, C.R.:
On testing for serial correlation in large number of small samples using tail probability approximations,
Bulletin of the International Statistical Institute,
ISI 54th Session,
ISI (ed.),
54th Session,
ISI,
Berlin,
(2003),
232–233.
90.
Peiris MS, Allen DE, Yang W
Shelton Peiris, David Allen, and Wenling Yang:
Some statistical models for durations and their applications in finance,
Modsim,
International Congress on Modelling and Simulation,
2003,
Modelling and Simulation Society of Australia and New Zealand Inc.,
Australia,
(2003),
1210–1214.
ISBN 174052 098X.
91.
Hunt RL, Peiris MS, Weber NC
Hunt, R. L., Peiris, M. S. and Weber, N. C.:
The bias of lag window estimators of the fractional difference parameter,
Journal of Applied Mathematics and Computing,
12
(2003),
67–79.
2004a:62156
92.
Peiris MS
M.S.Peiris:
A way of teaching statistics: An approach to flexible learning,
CAL-laborate,
9
(2002),
13–15.
93.
Peiris MS
M.S.Peiris:
Teaching Mathematical Statistic,
Scholarly Inquiry in Flexible ScienceTeaching and Learning,
Flexible Science Teaching and Learning,
2002,
UniServe Science,
Sydney University,
(2002),
85–86.
ISBN 1 86487 4902.
94.
Singh N, Vadavalli VSS, Peiris MS
N.Singh, V.S.S.Vadavalli, M.S.Peiris:
A Note on the Modelling and Analysis of Vector ARMA Processes with Nonstationary Innovations,
Mathematical and Computer Modelling,
36
(2002),
1409–1424.
2003k:62240
95.
Peiris MS, Thavaneswaran A
M.S. Peiris and A. Thavaneswaran:
On the properties of some nonstationary ARMA processes with infinite variance,
International Journal of Modelling and Simulation,
21
(2001),
301–304.
96.
Peiris MS, Thavaneswaran A
M.S. Peiris and A. Thavaneswaran:
Multivariate stable ARMA Processes with time dependent coefficients,
Metrika,
54
(2001),
no. 2,
131–138.
2002i:62166
97.
Peiris MS, Thavaneswaran A
M.S. Peiris and A. Thavaneswaran:
Recursive estimation for regression with infinite variance fractional ARIMA noise,
Mathematical and Computer Modelling,
34
(2001),
1133–1137.
MR1858841
98.
Thavaneswaran A, Peiris MS
A Thavaneswaran and S Peiris:
Inference for some time series models with random coefficients and infinite variance,
Mathematical and Computer Modelling,
33
(2001),
843–849.
MR1826538
99.
Thavaneswaran A, Peiris MS
A Thavaneswaran, S Peiris:
Estimation for regression with infinite variance errors,
Mathematical and Computer Modelling,
29
(1999),
177–180.
MR1704773
100.
Thavaneswaran A, Peiris MS
A Thavaneswaran, Shelton Peiris:
Hypothesis testing for some time-series models: a power comparison,
Statistics and Probability Letters,
38
(1998),
151–156.
99e:62166
101.
Abraham B, Thavaneswaran A, Peiris MS
Bovas Abraham, A. Thavaneswaran and Shelton Peiris:
On the prediction scheme for some nonlinear time series models using estimating functions,
Selected Proceedings of the Symposium on Estimating Functions,
Symposium on Estimating Functions,
Ishwar V. Basawa, V.P. Godambe and Robert Taylor (eds.),
Lecture Notes - Monograph Series,
Institute of Mathematical Statistics,
Hayward, California,
(1997),
259–271.
102.
Anh V, Lunney K, Peiris MS
V. Anh, K. Lunney, S. Peiris:
Stochastic models for characterisation and prediction of time series with long-range dependence and fractality,
Environmental Modelling and Software,
12
(1997),
no. 1,
67–73.
103.
Singha N, Peiris MS
N. Singha and M. S. Peiris:
A simulation study on vector arma processes with nonstationary innovation: a new approach to identification,
Journal of Statistical Computation and Simulation,
58
(1997),
37–58.
104.
Peiris MS, Singha N
M. Shelton Peiris and N. Singh:
Predictors for seasonal and nonseasonal fractionally integrated arima models,
Biometrical Journal,
38
(1996),
741–752.
99c:62260
105.
Peiris MS
M. Shelton Peiris:
Improving the precision of forecasting,
Microelectronics and Reliability,
36
(1996),
1375–1378.
106.
Thavaneswaran A, Peiris MS
A. Thavaneswaran and Shelton Peiris:
Nonparametric estimation for some nonlinear models,
Statistics and Probability Letters,
28
(1996),
227–233.
97e:62113
107.
Poznanski RR, Peiris MS
Roman R. Poznanski and M. Shelton Peiris:
Subthreshold response to white-noise current input in a tapering cable model of a neuron,
IMA Journal of Mathematics Applied In Medicine and Biology,
13
(1996),
207–222.
108.
Peiris MS
Peiris MS:
Some Aspects of Forecasting with Vector MA Processes,
Bulletin of the Calcutta Statistical Association,
44
(1995),
175–176.
109.
Rai SN, Abraham B, Peiris MS
Rai SN, Abraham B, Peiris MS:
Analysis of Short Time Series with Over-dispersion Model,
Communications in Statistics. Theory and Methods,
24
(1995),
no. 2,
335–348.
MR1345662
110.
Chen G, Abraham B, Peiris MS
Chen G, Abraham B, Peiris MS:
Lag window estimation of the degree of differencing in fractionally integrated time series models,
Journal of Time Series Analysis,
15
(1994),
no. 5,
473–487.
MR1292162
111.
Peiris MS, Court JR
Peiris MS, Court JR:
A note on the estimation of the degree of differencing in long memory time series,
Journal of Probability and Mathematical Statistics,
14
(1993),
no. 2,
223–229.
96b:62145
112.
Peiris MS
Peiris MS:
Some aspects of forecasting with vector ARMA processes,
11th Australian Statistical Conference, University of WA, Perth,
(1992),
113.
Peiris MS
Peiris MS:
Some non-stationary ARMA models,
Advances in Modelling and Simulation,
27
(1991),
21–34.
114.
Peiris MS
Peiris MS:
Multivariate ARMA processes with nonstationary innovations,
10th Australian Statistical Conference, University of NSW,
(1990),
115.
Peiris MS
Peiris MS:
Analysis of multivariate ARMA processes with nonstationary innovations,
Communications in Statistics. Theory and Methods,
19
(1990),
2847–2852.
92b:62135
116.
Peiris MS, Singh N
Peiris MS, Singh N:
Optimal experimental design for linear time series models with stochastic coefficients,
Journal of the Indian Society for Statistics and Operations Research,
10
(1989),
1–4.
MR1201934
117.
Peiris MS, Perera BJC
Peiris MS, Perera BJC:
On the prediction with fractionally differenced ARMA models,
Journal of Time Series Analysis,
9
(1988),
215–220.
90a:62250
118.
Peiris MS
Peiris MS:
On the study of some functions of Multivariate ARMA processes,
Journal of Multivariate Analysis,
25
(1988),
no. 1,
146–151.
89d:62094
119.
Peiris MS, Singh N
Peiris, M.S. and Singh, N.:
Optimal experimental design for linear time series models with stochastic coefficients,
Journal of the Indian Society for Statistics and Operations Research,
8
(1987),
1–9.
MR0918016
120.
Peiris MS, Singh N
Peiris MS, Singh N:
On prediction of multivariate ARMA processes with a time dependent covariance structure,
Communications in Statistics. Theory and Methods,
17
(1987),
27–37.
MR0951704
121.
Peiris MS, Singh N
Peiris MS, Singh N:
A simple and asymptotically optimal test of equality for \(q > 2\) multivariate normal distributions: A pragmatic approach to one way classification,
Microelectronics and Reliability,
27
(1987),
567–573.
122.
Peiris MS, Singh N
Peiris MS, Singh N:
A note on the properties of some nonstationary ARMA processes,
Stochastic Processes and their Applications,
24
(1987),
151–155.
88g:60099
123.
Peiris MS
Peiris MS:
A note on the predictors of differenced sequences,
The Australian Journal of Statistics,
29
(1987),
42–48.
88g:62202
124.
Peiris MS
Peiris MS:
On prediction with time dependent ARMA models,
Communications in Statistics. Theory and Methods,
15
(1986),
3659–3668.
MR0871332
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