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Obituary: Richard Lewis Tweedie
Published online by Cambridge University Press: 14 July 2016
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References
References
Publication of R.L. Tweedie
Item authored or co-authored by R.L. Tweedie, listed chronologically.
[1]
Tweedie, R. L. (1971). Stationary measures for random walk on a half line. Z. Wahrscheinlichkeitsth.
20, 28–46.Google Scholar
[2]
Tweedie, R. L. (1971). Truncation procedures for non-negative matrices. J. Appl. Prob.
8, 311–320.Google Scholar
[3]
Tweedie, R. L. (1973). The calculation of limit probabilities for denumerable Markov processes from infinitesimal properties. J. Appl. Prob.
10, 84–99.CrossRefGoogle Scholar
[4]
Tweedie, R. L. (1974). A representation for invariant measures for transient Markov chains. Z. Wahrschein-lichkeitsth.
28, 99–112.CrossRefGoogle Scholar
[5]
Tweedie, R. L. (1974). Some ergodic properties of the Feller minimal process. Quart. J. Math. Oxford
25, 485–495.Google Scholar
[6]
Tweedie, R. L. (1974).
R-theory for Markov chains on a general state-space. I. Solidarity properties and R-recurrent chains. Ann. Prob.
2, 840–864.Google Scholar
[7]
Tweedie, R. L. (1974).
R-theory for Markov chains on a general state-space. II. r-subinvariant measures for r-transient chains. Ann. Prob.
2, 865–878.Google Scholar
[8]
Tweedie, R. L. (1974). Quasi-stationary distributions for Markov chains on a general state-space. J. Appl. Prob.
11, 726–741.Google Scholar
[9]
Tweedie, R. L. (1975). Truncation approximation of the limit probabilities for denumerable semi-Markov processes. J. Appl. Prob.
12, 161–163.CrossRefGoogle Scholar
[10]
Pollard, D. B. and Tweedie, R. L. (1975).
R-theory for Markov chains on a topological state-space. I. J. London Math. Soc.
10, 389–400.Google Scholar
[11]
Tweedie, R. L. (1975). Sufficient conditions for regularity, recurrence, and ergodicity of Markov processes. Math. Proc. Camb. Phil. Soc.
78, 125–136.CrossRefGoogle Scholar
[12]
Tweedie, R. L. (1975). Sufficient conditions for ergodicity and recurrence of Markov chains on a general state-space. Stoch. Process. Appl.
3, 385–403.Google Scholar
[13]
Tweedie, R. L. (1975). The robustness of positive recurrence and recurrence of Markov chains under perturbations of the transition probabilities. J. Appl. Prob.
12, 744–752.Google Scholar
[14]
Tweedie, R. L. (1975). Relations between ergodicity and mean drift for Markov chains. Austral. J. Statist.
17, 96–102.CrossRefGoogle Scholar
[15]
Saunders, I. W. and Tweedie, R. L. (1976). The settlement of Polynesia by CYBER 76. Math. Scientist
1, 15–26.Google Scholar
[16]
Pollard, D. B. and Tweedie, R. L. (1976).
R-theory for Markov chains on a topological state-space. II. Z. Wahrscheinlichkeitsth.
34, 269–278.CrossRefGoogle Scholar
[17]
Tweedie, R. L. (1976). Criteria for classifying general Markov chains. Adv. Appl. Prob.
8, 737–771.Google Scholar
[18]
McArthur, N. A., Saunders, I. W. and Tweedie, R. L. (1976). Small population isolates: a simulation study. J. Polynesian Soc.
85, 307–326.Google Scholar
[19]
Nummelin, E. and Tweedie, R. L. (1976). Geometric ergodicity for a class of Markov chains. Ann. Sci. Univ. Clermont
61, 145–153.Google Scholar
[20]
Tweedie, R. L. (1977). Modes of convergence of Markov chain transition probabilities. J. Math. Anal. Appl.
60, 280–291.Google Scholar
[21]
Tweedie, R. L. (1977). Hitting times for Markov chains with application to state dependent queues. Bull. Austral. Math. Soc.
17, 97–107.Google Scholar
[22]
Tweedie, R. L. (1977). Criteria for classifying general Markov chains (abstract). Adv. Appl. Prob.
9, 208–210.Google Scholar
[23]
Tweedie, R. L. and Westcott, M. (1978). First-passage times in skip-free processes. Stoch. Process. Appl.
7, 191–204.Google Scholar
[24]
Nummelin, E. and Tweedie, R. L. (1978). Geometric ergodicity and R-positivity for general Markov chains. Ann. Prob.
6, 404–420.Google Scholar
[25]
Isaacson, D. and Tweedie, R. L. (1978). Criteria for strong ergodicity of Markov chains. J. Appl. Prob.
15, 87–95.Google Scholar
[26]
Arjas, E., Nummelin, E. and Tweedie, R. L. (1978). Uniform limit theorems for non-singular renewal and Markov renewal processes. J. Appl. Prob.
15, 112–125.Google Scholar
[27]
Laslett, G. M., Pollard, D. B. and Tweedie, R. L. (1978). Techniques for establishing ergodic and recurrent properties of continuous-valued Markov chains. Naval. Res. Logistics Quart.
25, 455–472.Google Scholar
[28]
Tweedie, R. L. (ed.) (1978). Proc. Conf. Spatial Patterns Process., Canberra, 12-14 May 1977 (Adv. Appl. Prob. 10 Spec. Suppl.). Applied Probability Trust, Sheffield.Google Scholar
[29]
Tuominen, P. and Tweedie, R. L. (1979). Markov chains with continuous components. Proc. London Math. Soc.
38, 89–114.CrossRefGoogle Scholar
[30]
Tweedie, R. L. (1979). Topological aspects of Doeblin decompositions for Markov chains. Z. Wahrschein-lichkeitsth.
46, 299–305.Google Scholar
[31]
Tuominen, P. and Tweedie, R. L. (1979). The recurrence structure of general Markov processes. Proc. London Math. Soc.
39, 554–576.Google Scholar
[32]
Tweedie, R. L. (1979). Computerised anthropology—finding and settling Polynesian islands. Math. Spectrum
11, 75–81.Google Scholar
[33]
Schuh, H.-J. and Tweedie, R. L. (1979). Parameter estimation using transform estimation in time-evolving models. Math. Biosci.
45, 37–67.Google Scholar
[34]
Tuominen, P. and Tweedie, R. L. (1979). Exponential decay and ergodicity of general Markov processes and their discrete skeletons. Adv. Appl. Prob.
11, 784–803.Google Scholar
[35]
Tuominen, P. and Tweedie, R. L. (1979). Exponential ergodicity in Markovian queueing and dam models. J. Appl. Prob.
16, 867–880.Google Scholar
[36]
Arjas, E., Nummelin, E. and Tweedie, R. L. (1980). Semi-Markov processes on a general state-space: a-theory and quasi-stationary properties. J. Austral. Math. Soc. A
30, 187–200.Google Scholar
[37]
Athreya, K. B., Tweedie, R. L. and Vere-Jones, D. (1980). Asymptotic behaviour of point processes with Markov-dependent intervals. Math. Nachr.
99, 301–313.Google Scholar
[38]
Tweedie, R. L. (1980). Perturbations of countable Markov chains. Ann. Inst. Statist. Math. A.
32, 283–290.Google Scholar
[39]
Young, R. R., Anderson, N., Overend, D., Tweedie, R. L., Malafant, K. W. J. and Preston, G. A. N. (1980). The effect of temperature on times to hatching of eggs of the nematode Ostertagia Circumcinta
. Parasitology
81, 477–491.Google Scholar
[40]
Young, R. R., Nicholson, R. M., Tweedie, R. L. and Schuh, H.-J. (1980). Quantitative modeling and prediction of development times of the free living stages of Ostertagia ostertagi under controlled and field conditions. Parasitology
81, 493–505.Google Scholar
[41]
Low, W. A., Tweedie, R. L., Edwards, C. B. H., Hodder, R. M., Malafant, K. W. J. and Cunningham, R. B. (1981). The influence of environment on daily maintenance behaviour of free-ranging shorthorn cows in Central Australia. I: general introduction and descriptive analysis of day-long activities. Appl. Animal Ethol.
7, 11–26.Google Scholar
[42]
Low, W. A., Tweedie, R. L., Edwards, C. B. H., Hodder, R. M., Malafant, K. W. J. and Cunningham, R. B. (1981). The influence of environment on daily maintenance behaviour of free-ranging shorthorn cows in Central Australia. II: multivariate analysis of duration and incidence of activities. Appl. Animal Ethol.
7, 27–38.Google Scholar
[43]
Low, W. A., Tweedie, R. L., Edwards, C. B. H., Hodder, R. M., Malafant, K. W. J. and Cunningham, R. B. (1981). The influence of environment on daily maintenance behaviour of free-ranging shorthorn cows in Central Australia. III: detailed analysis of sequential behavior patterns and integrated discussion. Appl. Animal Ethol.
7, 39–56.CrossRefGoogle Scholar
[44]
Tweedie, R. L. (1981). Criteria for ergodicity, exponential ergodicity and strong ergodicity of Markov processes. J. Appl. Prob.
18, 122–130.Google Scholar
[45]
Tweedie, R. L. (1982). Operator-geometric stationary distributions for Markov chains, with application to queueing models. Adv. Appl. Prob.
14, 368–391.CrossRefGoogle Scholar
[46]
Brockwell, P. J., Resnick, S. I. and Tweedie, R. L. (1982). Storage processes with general release rule and additive input. Adv. Appl. Prob.
14, 392–433.Google Scholar
[47]
Malafant, K. W. J., and Tweedie, R. L. (1982). Computer production of kinetograms. Appl. Animal Ethol.
8, 179–187.Google Scholar
[48]
Trajstman, A. C., and Tweedie, R. L. (1982). Techniques for estimating parameters in Bartoszynski's virus model. Math. Biosci.
58, 277–307.CrossRefGoogle Scholar
[49]
Tweedie, R. L. (1982). Criteria for rates of convergence of Markov chains, with application to queueing and storage theory. In Papers in Probability, Statistics and Analysis (London Math. Soc. Lecture Notes 79), eds Kingman, J. F. C. and Reuter, G. E. H., Cambridge University Press, pp. 260–276.Google Scholar
[50]
Feigin, P. D., Belyea, C. and Tweedie, R. L. (1983). Weighted area techniques for explicit parameter estimation in hierarchical models. Austral. J. Statist.
25, 1–16.Google Scholar
[51]
Theodorescu, R. and Tweedie, R. L. (1983). Solidarity properties and a Doeblin decomposition for a class of non-Markovian stochastic processes. Metrika
30, 37–47.CrossRefGoogle Scholar
[52]
Sennott, L. I., Humblet, P. and Tweedie, R. L. (1983). Mean drifts and the non-ergodicity of Markov chains. Operat. Res.
31, 783–789.Google Scholar
[53]
Tweedie, R. L. (1983). The existence of moments for stationary Markov chains. J. Appl. Prob.
20, 191–196.Google Scholar
[54]
Leedow, M. I. and Tweedie, R. L. (1983). Weighted area techniques for the estimation of the parameters of a growth curve. Austral. J. Statist.
25, 310–320.Google Scholar
[55]
Henstridge, J. D. and Tweedie, R. L. (1984). A model for the growth pattern of mutton birds. Biometrics
40, 917–925.Google Scholar
[56]
Tweedie, R. L. and Hall, N. (1984). A rotational sampling framework for NSW health statistics (abstract). Community Health Studies
8, 144.Google Scholar
[57]
Arjas, E., Haara, P. and Tweedie, R. L. (1984). A system model with interacting components: renewal-type results (abstract). Adv. Appl. Prob.
16, 7–8.Google Scholar
[58]
Feigin, P. D. and Tweedie, R. L. (1985). Random coefficient autoregressive processes: a Markov chain analysis of stationarity and finiteness of moments. J. Time Ser. Anal.
6, 1–14.Google Scholar
[59]
Seneta, E. and Tweedie, R. L. (1985). Moments for stationary and quasi-stationary distributions of Markov chains. J. Appl. Prob.
22, 148–155.Google Scholar
[60]
Arjas, E., Haara, P. and Tweedie, R. L. (1985). Reliability in multi-component systems: structure and convergence to stationary behaviour. Optimisation
16, 297–311.Google Scholar
[61]
Feigin, P. D. and Tweedie, R. L. (1985). Markov-chain ergodicity and time-series models (abstract). Stoch. Process. Appl.
19, 17.Google Scholar
[62]
Tweedie, R. L. (1986). The existence of moments for Markov and semi-Markov processes with application to birth-death and stress release models. In Proc. 1st Pacific Statist. Cong., eds Francis, I., Manly, B. J. F., Lam, F. C., North-Holland, Amsterdam, pp. 147–149.Google Scholar
[63]
Hall, J., Hall, N. and Tweedie, R. L. (1986). A longitudinal study of health changes following the introduction of Medicare. In Economics and Health 1985, Proc. 7th Austral. Conf. Health Econom. (Austral. Studies Health Service Administration 56), eds Butler, J.. and Doessel, D. P., University of New South Wales, pp. 81–102.Google Scholar
[64]
Tweedie, R. L. (1986). Recurrence criterion. In Encyclopaedia of Statistical Sciences, Vol. 7, eds Kotz, S., Johnson, N. L. and Read, C. B., John Wiley, New York, pp. 656–658.Google Scholar
[65]
Tweedie, R. L. (1986). Working with official statistics: some SIROMATH experiences. Austral. J. Statist.
28, 265–286.Google Scholar
[66]
Tweedie, R. L. (1986). In and out of applied probability in Australia. In The Craft of Probabilistic Modelling (Appl. Prob. Ser.), ed. Gani, J., Springer, New York, pp. 291–308.CrossRefGoogle Scholar
[68]
Tweedie, R. L. (1988). Invariant measures for Markov chains with no irreducibility assumptions. In A Celebration of Applied Probability (J. Appl. Prob. Spec. Vol. 25A), ed. Gani, J., Applied Probability Trust, Sheffield, pp. 275–285.Google Scholar
[69]
Tweedie, R. L. (1988). Return state. In Encyclopaedia of Statistical Sciences, Vol. 8, eds Kotz, S., Johnson, N. L. and Read, C. B., John Wiley, New York, pp. 124–125.Google Scholar
[70]
Tweedie, R. L. (1989). Shaping tomorrow's university. In Proc. SOST’89, eds Clark, R. and Cameron, J., ACS, Sydney, pp. 427–439.Google Scholar
[71]
Feigin, P. D. and Tweedie, R. L. (1989). Linear functionals and Markov chains associated with the Dirichlet process. Math. Proc. Camb. Phil. Soc.
105, 579–585.Google Scholar
[72]
Tweedie, R. L. (1989). Total quality management and information technology. Internat. J. Value-Based Manag.
2, 111–125.Google Scholar
[73]
Dunsmuir, W. T. M., Tweedie, R. L., Flack, L. and Mengersen, K. E. (1989). Modelling of transitions between employment states for young Australians. Austral. J. Statist.
31A, 165–196.Google Scholar
[74]
Tweedie, R. L. (1990). Criteria for rates of convergence to stationarity in Markovian queueing models with application to the GSPP/GSPP/1 queue. In Proc. 4th Austral. Teletraffic Res. Seminar, ed. Harris, R. J., Bond University.Google Scholar
[75]
Tweedie, R. L. (1991). Pitman Medal awarded to E. J. Hannan. Austral. J. Statist.
33, 1–4
Google Scholar
[76]
Brockwell, P. J., Liu, J. and Tweedie, R. L. (1992). On the existence of a stationary threshold autoregressive-moving average model. J. Time Ser. Anal.
13, 95–107.Google Scholar
[77]
Tweedie, R. L. (1992). Comments on the Committee for New Researchers Guidelines. Statist. Sci.
7, 263–264.Google Scholar
[78]
Meyn, S. P. and Tweedie, R. L. (1992). Stability of Markovian processes I: criteria for discrete-time chains. Adv. Appl. Prob.
24, 542–574.Google Scholar
[79]
Tweedie, R. L. and Mengersen, K. L. (1992). Lung cancer and passive smoking: reconciling the biochemical and epidemiological approaches. British J. Cancer
66, 700–705.Google Scholar
[80]
Meyn, S. P. and Tweedie, R. L. (1993). Markov Chains and Stochastic Stability. Springer, London.Google Scholar
[81]
Meyn, S. P. and Tweedie, R. L. (1993). The Doeblin decomposition. Contemp. Math. 149, 211–225.Google Scholar
[82]
Meyn, S. P. and Tweedie, R. L. (1993). Generalised resolvents and Harris recurrence of Markov processes. Contemp. Math. 149, 227–250.CrossRefGoogle Scholar
[83]
Meyn, S. P. and Tweedie, R. L. (1993). Stability of Markovian processes II: continuous-time and sampled chains. Adv. Appl. Prob. 25, 487–517.Google Scholar
[84]
Meyn, S. P. and Tweedie, R. L. (1993). Stability of Markovian processes III: Foster-Lyapunov criteria for continuous-time processes. Adv. Appl. Prob. 25, 518–548.Google Scholar
[85]
Tweedie, R. L., Zhu, ζ. Y. and Choy, S. L. (1993). Parameter estimation using Laplace transforms in the M/M/1 queue. Tech. Rep. 93/6, Department of Statistics, Colorado State University.Google Scholar
[86]
Tweedie, R. L. (1994). Computable convergence rates and geometric ergodicity for Markovian queueing systems. In Proc. 31st Allerton Conf. Commun. Control Comput. (October 1993), eds Sarwate, D. P. and van Dooren, P., University of Illinois Press, Champaign, IL, pp. 404–412.Google Scholar
[87]
Meyn, S. P. and Tweedie, R. L. (1994). State-dependent criteria for convergence of Markov chains. Ann. Appl. Prob. 4, 149–168.Google Scholar
[88]
Spieksma, F. M. and Tweedie, R. L. (1994). Strengthening ergodicity to geometric ergodicity for Markov chains. Commum. Statist. Stoch. Models
10, 45–74.Google Scholar
[89]
Tweedie, R. L., Mengersen, K. L. and Eccleston, J. A. (1994). Garbage in, garbage out: can statisticians quantify the effects of poor data?
Chance
7, 20–27.Google Scholar
[90]
Tweedie, R. L. (1994). Topological conditions enabling use of Harris methods in discrete and continuous time. Acta Appl. Math. 34, 175–188.Google Scholar
[91]
Stramer, O. and Tweedie, R. L. (1994). Stability and instability of continuous time Markov processes. In Probability, Statistics and Optimization: a Tribute to Peter Whittle, ed. Kelly, F. P., John Wiley, London, pp. 173–183.Google Scholar
[92]
Meyn, S. P. and Tweedie, R. L. (1994). Computable bounds for geometric convergence rates of Markov chains. Ann. Appl. Prob. 4, 981–1011.Google Scholar
[93]
Meyn, S. P. and Tweedie, R. L. (1994). A survey of Foster-Lyapunov conditions for general state-space Markov processes. In Proc. Workshop Stoch. Stability Stoch. Stabilization (Metz, France, June 1993), Springer, Berlin.Google Scholar
[94]
Tuominen, P. and Tweedie, R. L. (1994). Subgeometric rates of convergence of f-ergodic Markov chains. Adv. Appl. Prob. 26, 775–798.Google Scholar
[95]
Biggerstaff, B., Tweedie, R. L. and Mengersen, K. L. (1994). Passive smoking in the workplace: classical and Bayesian meta-analyses. Internat. Arch. Occupational Environmental Health
66, 269–277.Google Scholar
[96]
Tweedie, R. L. and Mengersen, K. L. (1995). Meta-analytic approaches to dose-response relationships, with application in studies of lung cancer and exposure to environmental tobacco smoke. Statist. Medicine
14, 545–569.Google Scholar
[97]
Mengersen, K. L., Tweedie, R. L. and Biggerstaff, B. (1995). The impact of method choice in meta-analysis. Austral. J. Statist. 37, 19–44.Google Scholar
[98]
Down, D., Meyn, S. P. and Tweedie, R. L. (1995). Exponential and uniform ergodicity of Markov processes. Ann. Prob. 23, 1671–1691.Google Scholar
[99]
Mengersen, K. L. and Tweedie, R. L. (1995). Comments on ‘Convergence of Markov chain Monte Carlo algorithms’ by N. G. Polson. In Bayesian Statistics 5, Proc. 5th Valencia Internat. Meeting (5-9 June 1994, Alicante), eds Bernardo, J. M., Berger, J. O., Dawid, A. P. and Smith, A. F. M., Oxford University Press, pp. 317–318.Google Scholar
[100]
Mengersen, K. L. and Tweedie, R. L. (1996). Rates of convergence of the Hastings and Metropolis algorithms. Ann. Statist. 24, 101–121.Google Scholar
[101]
Lund, R. and Tweedie, R. L. (1996). Geometric convergence rates for stochastically ordered Markov chains. Math. Operat. Res. 21, 182–194.Google Scholar
[102]
Roberts, G. O. and Tweedie, R. L. (1996). Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms. Biometrika
83, 95–110.Google Scholar
[103]
Tweedie, R. L., Scott, D. J., Biggerstaff, B. J. and Mengersen, K. L. (1996). Bayesian meta-analysis, with application to studies of ETS and lung cancer. Lung Cancer
14, S171–S194.Google Scholar
[104]
Stramer, O., Brockwell, P. J. and Tweedie, R. L. (1996). Continuous-time threshold AR(1) models. Adv. Appl. Prob. 28, 728–746.Google Scholar
[105]
Stramer, O., Tweedie, R. L. and Brockwell, P. J. (1996). Existence and stability of continuous time threshold ARMA precesses. Statist. Sinica
6, 715–732.Google Scholar
[106]
Meyn, S. P., Lund, R. and Tweedie, R. L. (1996). Rates of convergence of stochastically monotone Markov processes. Ann. Appl. Prob. 6, 218–237.Google Scholar
[107]
Scott, D. J. and Tweedie, R. L. (1996). Explicit rates of convergence of stochastically monotone Markov chains. In Proc. Athens Conf. Appl. Prob. Time Ser. Anal., eds Heyde, C. C., Prohorov, Yu. V., Pyke, R. and Rachev, S. T., Springer, New York, pp. 176–191.Google Scholar
[108]
Roberts, G. O. and Tweedie, R. L. (1996). Exponential convergence of Langevin diffusions and their discrete approximations. Bernoulli
2, 341–363.Google Scholar
[109]
Tweedie, R. L. (1996). Electronic publishing in the IMS: a step forward. IMS Bull. 25, 627–629.Google Scholar
[110]
LaFleur, B., Taylor, S. J., Smith, D. D. and Tweedie, R. L. (1996). Bayesian assessment of publication bias in meta-analyses of cervical cancer and oral contraceptives. In Proc. Epidemiology Section 1996 Joint Statist. Meetings, American Statistical Association, Alexandria, VA, pp. 32–37.Google Scholar
[111]
Chen, P. and Tweedie, R. L. (1997). Orthogonal measures and absorbing sets for Markov chains. Math. Proc. Camb. Phil. Soc. 121, 101–113.Google Scholar
[112]
Biggerstaff, B. J. and Tweedie, R. L. (1997). Incorporating variability in estimates of heterogeneity in the random effects model in meta-analysis. Statist. Medicine
16, 753–768.Google Scholar
[113]
Tweedie, R. L. (1997). Annals of Applied Probability. In Encyclopedia of Statistical Sciences, Update Vol. 1, eds Kotz, S., Read, C. B. and Banks, D. L., John Wiley, New York, pp. 30–32.Google Scholar
[114]
Stramer, O. and Tweedie, R. L. (1997). Existence and stability of weak solutions to stochastic differential equations with non-smooth coefficients. Statist. Sinica
7, 577–594.Google Scholar
[115]
Taylor, S. J. and Tweedie, R. L. (1997). Assessing sensitivity to multiple factors in calculating attributable risk. Environmetrics
8, 351–372.3.0.CO;2-3>CrossRefGoogle Scholar
[116]
Givens, G. E., Smith, D. D. and Tweedie, R. L. (1997). Publication bias in meta-analysis: a Bayesian data-augmentation approach to account for issues exemplified in the passive smoking debate (with discussion). Statist. Sci. 12, 221–250.Google Scholar
[117]
Tweedie, R. L. (1998). Consulting: real problems, real interactions, real outcomes. Statist. Sci. 13, 1–3.Google Scholar
[118]
Hall, N. and Tweedie, R. L. (1998). Queueing at the tax office. Statist. Sci. 13, 18–23.Google Scholar
[119]
Tweedie, R. L. (1998). Assessing sensitivity to data problems in epidemiological meta-analyses. Proc. 51st Session ISI (Istanbul, August 1997), Vol. 3, International Statistical Institute, Amsterdam, pp. 556–559.Google Scholar
[120]
Foss, S. G. and Tweedie, R. L. (1998). Perfect simulation and backward coupling. Commun. Statist. Stoch. Models
14, 187–203.Google Scholar
[121]
Tweedie, R. L. (1998). Roles for societies in statistical education statistical education—expanding the network. In Proc. 5th ICOTS, ed. Pereira-Mendoza, L., International Statistical Institute, Amsterdam, pp. 439–444.Google Scholar
[122]
Foss, S. G., Tweedie, R. L. and Corcoran, J. N. (1998). Simulating the invariant measures of Markov chains using backward coupling at regeneration time. Prob. Eng. Inf. Sci. 12, 303–320.Google Scholar
[123]
Tweedie, R. L. (1998). Truncation approximations of invariant measures for Markov chains. J. Appl Prob. 35, 517–536.Google Scholar
[124]
Duval, S. and Tweedie, R. L. (1998). Practical estimates of the effect of publication bias in meta-analysis. Austral. Epidemiol. 5, 14–17.Google Scholar
[125]
Roberts, G. O. and Tweedie, R. L. (1999). Bounds on regeneration times and convergence rates for Markov chains. Stoch. Process. Appl. 80, 211–229. (Corrigendum: 91 (2001), 337-338.)Google Scholar
[126]
Mengersen, K. L., Merrilees, M. and Tweedie, R. L. (1999). Environmental tobacco smoke and ischaemic heart disease: a case study in applying causal criteria. Internat. Arch. Occupational Environmental Health
72, Suppl.
R1–R40.Google Scholar
[127]
Mengersen, K. L. and Tweedie, R. L. (1999). Calculating accuracy rates from multiple assessors with limited information. Amer. Statistician
53, 233–238.Google Scholar
[128]
Stramer, O. and Tweedie, R. L. (1999). Langevin-type models I: diffusions with given stationary distributions, and their discretizations. Methodol. Comput. Appl. Prob. 1, 283–306.Google Scholar
[129]
Stramer, O. and Tweedie, R. L. (1999). Langevin-type models II: self-targeting candidates for MCMC algorithms. Methodol. Comput. Appl. Prob. 1, 307–328.Google Scholar
[130]
Smith, D. D., Givens, G. E. and Tweedie, R. L. (2000). Adjustment for publication bias and quality bias in Bayesian meta-analysis. In Meta-analysis in Medicine and Health Policy, eds Berry, D. and Stangl, D., Marcel Dekker, New York, pp. 277–304.Google Scholar
[131]
Duval, S. J. and Tweedie, R. L. (2000). A nonparametric ‘trim and fill’ method of assessing publication bias in meta-analysis. J. Amer. Statist. Assoc. 95, 89–98.Google Scholar
[132]
Duval, S. J. and Tweedie, R. L. (2000). Trim and fill: a simple funnel plot based method of testing and adjusting for publication bias in meta-analysis. Biometrics, 56, 276–284.Google Scholar
[133]
Sutton, A. J., Duval, S. J., Tweedie, R. L., Abrams, K. R. and Jones, D. R. (2000). Empirical assessment of effect of publication bias on meta-analyses. British Medical J. 320, 1574–1577.Google Scholar
[134]
Roberts, G. O. and Tweedie, R. L. (2000). Rates of convergence of stochastically monotone stochastic processes. J. Appl. Prob. 37, 359–373.Google Scholar
[135]
Grunwald, G. K., Hyndman, R. J., Tedesco, L. and Tweedie, R. L. (2000). Non-Gaussian conditional linear AR(1) models. Austral. N. Z. J. Statist. 42, 479–495.Google Scholar
[136]
Sexton, K., Greaves, I. A., Church, T. R., Adgate, J. L., Ramachandran, G., Tweedie, R. L., Fredrickson, A.
Geisser, M., Sikorski, M., Fischer, G., Jones, D. and Ellringer, P. (2000). A school-based strategy to assess children's environmental exposures and related health effects in economically disadvantaged urban neighborhoods. J. Exposure Anal. Environmental Epidemiol. 10, 682–694.Google Scholar
[137]
Tweedie, R. L. (2001). Markov chains: structure and applications. In Handbook of Statistics, Vol. 19, Stochastic Processes: Theory and Methods, eds Shanbhag, D. N. and Rao, C. R., Elsevier, Amsterdam, pp. 817–851.Google Scholar
[138]
Corcoran, J. N. and Tweedie, R. L. (2001). Perfect sampling for ergodic Harris chains. Ann. Appl. Prob. 11, 438–451.Google Scholar
[139]
Tweedie, R. L. (2001). Drift conditions and invariant measures for Markov chains. Stoch. Process. Appl. 92, 345–354.Google Scholar
[140]
Roberts, G. O. and Tweedie, R. L. (2001). Geometric L2 and L1 convergence are equivalent for reversible Markov chains. In Probability, Statistics and Seismology (J. Appl. Prob. Spec. Vol. 38A), ed. Daley, D. J., Applied Probability Trust, Sheffield, pp. 37–41.Google Scholar
[141]
Wall, M., Boen, J. and Tweedie, R. L. (2001). An effective CI for the mean with samples of size 1 and 2. Amer. Statistician
55, 102–105.Google Scholar
[142]
Tweedie, R. L. and Corcoran, J. N. (2001). Perfect sampling and queuing models. In Proc. 38th Annual Allerton Conf. Commun. Control Comput. (4-6 October 2000), University of Illinois Press, Champaign, IL, pp. 116–124.Google Scholar
[143]
Henderson, S. and Tweedie, R. L. (2001). Perfect sampling for Markov chains arising from discrete event simulation. In Proc. 38th Annual Allerton Conf. Commun. Control Comput. (4-6 October 2000), University of Illinois Press, Champaign, IL, pp. 1125–1134.Google Scholar
[144]
Guglielmi, A. and Tweedie, R. L. (2001). Markov chain Monte Carlo estimation of the law of the mean of a Dirichlet process. Bernoulli
4, 573–592.Google Scholar
[145]
Jarner, S. F. and Tweedie, R. L. (2001). Locally contracting iterated functions and stability of Markov chains. J. Appl. Prob. 38, 494–507.Google Scholar
[146]
Stramer, O. and Tweedie, R. L. (2001). Existence and stability of weak solutions to stochastic differential equations with non-smooth coefficients. Statist. Sinica
7, 577–593.Google Scholar
[147]
Tweedie, R. L. (2001). Meta-analysis: overview. In International Encyclopedia of Social and Behavioral Sciences, Vol. 14, eds Smelser, N. J. and Baltes, P. B., Elsevier, Amsterdam, pp. 9717–9724.Google Scholar
[148]
Hoeting, J. A. and Tweedie, R. L. (2001). Parameter estimation for models of biological stage-frequency data, In Proc. Graybill Conf. Linear Models (13-15 June 2001, Colorado State University).Google Scholar
[149]
Hoeting, J. A. and Tweedie, R. L. (2001). Transform estimation of parameters for stage-frequency data. Tech. Rep. 2001-2, Department of Statistics, Colorado State University. Available at http://www.stat.colostate.edu/Documents/.Google Scholar
[150]
Hoeting, J. A., Johnson, D. S. and Tweedie, R. L. (2001). Empirical transform estimation of parameters in the monomolecular growth model. Tech. Rep. 2001-5, Department of Statistics, Colorado State University. Available at http://www.stat.colostate.edu/Documents/.Google Scholar
[151]
Fonseca, G. and Tweedie, R. L. (2001). Stationary measures for non-irreducible non-continuous Markov chains with time series applications. Submitted.Google Scholar
[152]
Jarner, S. F. and Tweedie, R. L. (2001). Stability properties of Markov chains defined via iterated random functions. Submitted.Google Scholar
[153]
Cooper, W. L. and Tweedie, R. L. (2001). Perfect simulation of an inventory model for perishable products. Submitted.Google Scholar
[154]
Tweedie, R. L. (2001). Convergence of Markov chains from all starting points, with applications to Metropolis-Hastings algorithms. Submitted.Google Scholar
[155]
Saleh, K. J., Dykes, D. C., Ravichandran, A., Saleh, R. M., Mohamed, K., Gioe, T. J., Heck, D. A. and Tweedie, R. L. (2001). Functional outcome following total knee athroplasty revision: a meta-analysis. Submitted.Google Scholar
[156]
Jarner, S. F. and Tweedie, R. L. (2001). Convergence rates and moments of Markov chains associated with the mean of Dirichlet processes. Submitted.Google Scholar
[157]
Jarner, S. F. and Tweedie, R. L. (2001). Necessary conditions for geometric and polynomial ergodicity of random walk-type Markov chains. Submitted.Google Scholar
[158]
Tweedie, R. L. (2002). Meta-analysis. In Encyclopedia of Environmetrics, eds El-Shaarawi, A. H. and Piegorsch, W. W., John Wiley, New York.Google Scholar
[159]
Tweedie, R. L. (2002). Environmental tobacco smoke. In Encyclopedia of Environmetrics, eds El-Shaarawi, A. H. and Piegorsch, W. W., John Wiley, New York.Google Scholar
[160]
Tweedie, R. L. and Corcoran, J. N. (2002). Perfect sampling from independent Metropolis-Hastings chains. J. Statist. Planning Infer. 104, 297–314.Google Scholar
Other reference
[161]
Breyer, L., and Roberts, G. O. (2001). Catalytic perfect simulation. Methodol. Comput. Appl. Prob.
3, 161–177.Google Scholar
[162]
Dai, J. G. (1996). A fluid-limit model criterion for instability of multiclass queueing networks. Ann. Appl. Prob.
6, 751–757.Google Scholar
[163]
De Acosta, A., and Ney, P. (1998). Large deviation lower bounds for arbitrary additive functionals of a Markov chain. Ann. Prob.
26, 1660–1682.Google Scholar
[164]
Doeblin, W. (1940) Éléments d'une théorie générale des chaînes simples constantes de Markoff. Ann. École Norm. (3)
57, 61–111.Google Scholar
[165]
Hasḿinskiĭ, R. Z. (1980). Stochastic Stability Of Differential Equations. Sijthoff and Noordhoff, Alphen aan den Rijn. (Translated from the Russian by D. Louvish.)Google Scholar
[166]
Hordijk, A., and Spieksma, F. M. (1992). On ergodicity and recurrence properties of a Markov chain with an application. Adv. Appl. Prob.
24, 343–376.Google Scholar
[167]
Kontoyiannis, I., and Meyn, S. P. (2002). Spectral theory and limit theorems for geometrically ergodic Markov processes. To appear in Ann. Prob.Google Scholar
[168]
Kumar, P. R., and Meyn, S. P. (1996). Duality and linear programs for stability and performance analysis of queuing networks and scheduling policies. IEEE Trans. Automatic Control
41, 4–17.Google Scholar
[169]
Malyšev, V. A. (1972). Classification of two-dimensional positive random walks and almost linear semi-martingales. Soviet Math. Dokl.
13, 136–139.Google Scholar
[170]
Ney, P., and Nummelin, E. (1987). Markov additive processes I: eigenvalue properties and limit theorems. Ann. Prob.
15, 561–592.Google Scholar
[171]
Ney, P., and Nummelin, E. (1987) Markov additive processes II: large deviations. Ann. Prob.
15, 593–609.Google Scholar
[172]
Nummelin, E. (1984).
General Irreducible Markov Chains and Nonnegative Operators
. Cambridge University Press.Google Scholar
[173]
Orey, S. (1971).
Lecture Notes on Limit Theorems for Markov Chain Transition Probabilities (Van Nostrand Reinhold Math. Studies
34). Van Nostrand Reinhold, London.Google Scholar
[174]
Propp, J. G., and Wilson, D. B. (1996). Exact sampling with coupled Markov chains and applications to statistical mechanics. Random Structures Algorithms
9, 223–252.Google Scholar
[175]
Roberts, G. O. (2002). Linking theory and practice of MCMC. To appear in Highly Structured Stochastic Systems, eds Green, P., Hjort, N. and Richardson, S., Oxford University Press.Google Scholar
[176]
Roberts, G. O., and Rosenthal, J. S. (1999). Convergence of the slice sampler. J. R. Statist. Soc. B
61, 643–660.Google Scholar
[177]
Roberts, G. O., and Rosenthal, J. S. (2002). The polar slice sampler. To appear in Stoch. Models.Google Scholar
[178]
Seneta, E. (1981).
Nonnegative Matrices and Markov Chains, 2nd edn. Springer, New York.Google Scholar
[179]
Vere-Jones, D. (1967). Ergodic properties of nonnegative matrices. I. Pacific J. Math.
22, 361–386.Google Scholar
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