On composite marginal likelihoods

AStA Advances in Statistical Analysis

Volumn 92, Issue 1, 2008, Pages 1-28

  Varin, Cristiano ^a  

^a CA' FOSCARI UNIVERSITY OF VENICE   (Italy)

Author keywords

Clustered data; Composite likelihood; Computational efficiency; Genetics; Longitudinal data; Missing data; Pairwise likelihood; Pseudolikelihood; Spatial data; Statistical efficiency; Survival analysis; Time series

Indexed keywords

EID: 46649119286     PISSN: 18638171     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10182-008-0060-7     Document Type: Article

References (108)

1. Aerts, M., Claeskens, G.: Bootstrapping pseudolikelihood models for clustered binary data. Ann. Inst. Stat. Math. 51, 515-530 (1999)
2. Allcroft, D.J., Glasbey, C.A.: A spectral estimator of ARMA parameters from thresholded data. Stat. Comput. 12, 369-376 (2002)
3. Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: Petrov, B.N., Caski, F. (eds.) Proc. Second International Symposium on Information Theory, pp. 267-281. Akademiai Kiado, Budapest (1973)
4. Andersen, E.W.: Composite likelihood and two-stage estimation in family studies. Biostatistics 5, 15-30 (2004)
5. Anderson, J.A., Pemberton, J.D.: The grouped continuous model for multivariate ordered categorical variables and covariate adjustment. Biometrics 41, 875-885 (1985)
6. Apanasovich, T.V., Ruppert, D., Lupton, J.R., Popovic, N., Turner, N.D., Chapkin, R.S., Carroll, R.J.: Aberrant crypt foci and semiparametric modelling of correlated binary data. Biometrics (2008, to appear)
7. Azzalini, A.: Maximum likelihood of order m for stationary stochastic processes. Biometrika 70, 381-387 (1983)
8. Bellio, R., Varin, C.: A pairwise likelihood approach to generalized linear models with crossed random effects. Stat. Model. 5, 217-227 (2005)
9. Besag, J.E.: Spatial interaction and the statistical analysis of lattice systems (with discussion). J. R. Stat. Soc. B 34, 192-236 (1974)
10. Besag, J.E.: Efficiency of pseudolikelihood estimation for simple Gaussian fields. Biometrika 64, 616-618 (1977)
11. Bevilacqua, M., Gaetan, C., Mateu, J., Porcu, E.: Estimating space-time covariane functions: A composite likelihood approach (2007, submitted)
12. Breslow, N.E., Clayton, D.G.: Approximate inference in generalized linear mixed models. J. Am. Stat. Assoc. 88, 9-25 (1993)
13. Caragea, P., Smith, R.L.: Approximate likelihoods for spatial processes (2007, submitted)
14. Caragea, P., Smith, R.L.: Asymptotic properties of computationally efficient alternative estimators for a class of multivariate normal models. J. Multivar. Stat. Anal. (2008, to appear)
15. Carey, V., Zeger, S.L., Diggle, P.: Modelling multivariate binary data with alternating logistic regressions. Biometrika 80, 517-526 (1993)
16. Chandler, R.E., Bate, S.: Inference for clustered data using the independence loglikelihood. Biometrika 94, 167-183 (2007)
17. Christensen, O., Waagepetersen, R.: Bayesian prediction of spatial count data using generalized linear mixed models. Biometrics 58, 280-286 (2002)
18. Cox, D.R.: Partial likelihood. Biometrika 62, 269-276 (1975)
19. Cox, D.R., Reid, N.: A note on pseudolikelihood constructed from marginal densities. Biometrika 91, 729-737 (2004)
20. Cressie, N.: Statistics for Spatial Data, 2nd edn. Wiley, New York (1993)
21. Curriero, F.C., Lele, S.: A composite likelihood approach to semivariogram estimation. J. Agric. Biol. Environ. Stat. 4, 9-28 (1999)
22. Davison, A.C.: Statistical Models. Cambridge University Press, Cambridge (2003)
23. de Leon, A.R.: Pairwise likelihood approach to grouped continuous model and its extension. Stat. Probab. Lett. 75, 49-57 (2005)
24. de Leon, A.R., CarriEre, K.C.: General mixed-data model: extension of general location and grouped continuous models. Can. J. Stat. 35, 1-16 (2007)
25. Diggle, P.J.: Binary mosaics and the spatial pattern of heather. Biometrics 37, 531-539 (1981)
26. Diggle, P.J., Tawn, J.A., Moyeed, R.A.: Model-based geostatistics (with discussion). J. R. Stat. Soc. Ser. B 47, 299-350 (1998)
27. Drum, M.L., McCullagh, P.: REML estimation with exact covariance in the logistic mixed model. Biometrics 49, 677-689 (1993)
28. Engler, D.A., Mohapatra, M., Louis, D.N., Betensky, R.A.: A pseudolikelihood approach for simultaneous analysis of array comparative genomic hybridizations. Biostatistics 7, 399-421 (2006)
29. Fearnhead, P.: Consistency of estimators of the population-scaled recombination rate. Theor. Popul. Biol. 64, 65-79 (2003)
30. Fearnhead, P., Donnely, P.: Approximate likelihood methods for estimating local recombination rates (with discussion). J. R. Stat. Soc. B 64, 657-680 (2002)
31. Fieuws, S., Verbeke, G.: Pairwise fitting of mixed models for the joint modeling of multivariate longitudinal profiles. Biometrics 62, 424-431 (2006)
32. Fieuws, S., Verbeke, G., Boen, F., Delecluse, C.: High dimensional multivariate mixed models for binary questionnaire data. Appl. Stat. 55, 449-460 (2006)
33. Fieuws, S., Verbeke, G., Molenberghs, G.: Random-effects models for multivariate repeated measures. Stat. Methods Med. Res. 16, 387-397 (2007)
34. Gao, X., Song, P.X.-K., Pu, D.Q.: Composite likelihood inference for gene networks on time-course microarray data (2007, submitted)
35. Geys, H., Molenberghs, G., Ryan, L.M.: Pseudolikelihood modelling of multivariate outcomes in developmental toxicology. J. Am. Stat. Assoc. 94, 734-745 (1999)
36. Geys, H., Regan, M.M., Catalano, P.J., Molenberghs, G.: Two latent variable risk assessment approaches for mixed continuous and discrete outcomes from developmental toxicity data. J. Agric. Biol. Environ. Stat. 6, 340-355 (2001)
37. Glasbey, C.A.: Non-linear autoregressive time series with multivariate Gaussian mixtures as marginal distributions. Appl. Stat. 50, 143-154 (2001)
38. Godambe, V.P.: An optimum property of regular maximum likelihood equation. Ann. Math. Stat. 31, 1208-1211 (1960)
39. Guan, Y.: A composite likelihood approach in fitting spatial point process models. J. Am. Stat. Assoc. 101, 1502-1512 (2006)
40. Guan, Y.: A composite likelihood cross-validation approach in selecting bandwidth for the estimation of the pair correlation function. Scand. J. Stat. 34, 336-346 (2007)
41. Hall, P., Jing, B.: On sample reuse methods for dependent data. J. R. Stat. Soc. Ser. B 58, 727-737 (1996)
42. Hanfelt, J.J.: Composite conditional likelihood for sparse clustered data. J. R. Stat. Soc. Ser. B 66, 259-273 (2004)
43. Haslett, J., Raftery, A.E.: Space-time modelling with long-memory dependence: Assessing Ireland wind power resource. Appl. Stat. 38, 1-50 (1989)
44. Heagerty, P.J., Lele, S.R.: A composite likelihood approach to binary spatial data. J. Am. Stat. Assoc. 93, 1099-1111 (1998)
45. Heagerty, P.J., Lumley, T.: Window subsampling of estimating functions with application to regression models. J. Am. Stat. Assoc. 95, 197-211 (2000)
46. Heyde, C.C.: Quasi-Likelihood and its Application. Springer, New York (1997)
47. Henderson, R., Shimakura, S.: A serially correlated gamma frailty model for longitudinal count data. Biometrika 90, 355-366 (2003)
48. Hjort, N.L., Mohn, E.: Topics in the statistical analysis of remotely sensed data (with discussion). Bull. Int. Stat. Inst. 52, 23-44 (1987) (Proceedings of the ISI Meeting, Tokyo)
49. Hjort, N.L., Omre, H.: Topics in spatial statistics (with discussion). Scand. J. Stat. 21, 289-357 (1994)
50. Hjort, N.L., Varin, C.: ML, PL and QL for Markov chain models. Scand. J. Stat. 35, 64-82 (2008)
51. Hougaard, P.: A class of multivariate failure time distributions. Biometrika 73, 671-678 (1986)
52. Hudson, R.R.: Two-locus sampling distributions and their application. Genetics 159, 1805-1817 (2001)
53. Jöreskog, K.G., Moustaki, I.: Factor analysis of ordinal variables: a comparison of three approaches. Multivar. Behav. Res. 36, 347-386 (2001)
54. Kent, J.T.: Robust properties of likelihood ratio tests. Biometrika 69, 19-27 (1982)
55. Kuk, A.Y.C.: Permutation invariance of alternating logistic regression for multivariate binary data. Biometrika 91, 758-761 (2004)
56. Kuk, A.Y.C.: A hybrid pairwise likelihood method. Biometrika 94, 939-952 (2007)
57. Kuk, A.Y.C., Nott, D.J.: A pairwise likelihood approach to analyzing correlated binary data. Stat. Probab. Lett. 47, 329-335 (2000)
58. Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22, 79-86 (1951)
59. Le Cessie, S., Van Houwelingen, J.C.: Logistic regression for correlated binary data. Appl. Stat. 43, 95-108 (1994)
60. Lele, S.R.: Sampling variability and estimates of deAsity dependence: a composite-likelihood approach. Ecology 87, 189-202 (2006)
61. Lele, S.R., Taper, M.L.: A composite likelihood approach to (co)variance components estimation. J. Stat. Plan. Inference 103, 117-135 (2002)
62. Liang, K.-Y., Qin, J.: Regression analysis under non-standard situations: A pairwise pseudolikelihood approach. J. R. Stat. Soc. B 62, 773-786 (2000)
63. Liang, G., Yu, B.: Maximum pseudolikelihood estimation in network tomography. IEEE Trans. Signal Process. 51, 2043-2053 (2003)
64. Liang, K.-Y., Zeger, S.L.: Longitudinal data analysis using generalized linear models. Biometrika 73, 13-22 (1986)
65. Lindsay, B.G.: Composite likelihood methods. Contemp. Math. 80, 221-240 (1988)
66. Mardia, K.V., Hughes, G., Taylor, C.C.: Efficiency of the pseudolikelihood for multivariate normal and von Mises distributions (2007, submitted)
67. Mateu, J., Porcu, E., Christakos, G., Bevilacqua, M.: Fitting negative spatial covariances to geothermal field temperatures in Nea Kessani (Greece). Environmetrics 18, 759-773 (2007)
68. McCullagh, P., Nelder, J.A.: Generalised Linear Models, 2nd edn. Chapman and Hall, London (1989)
69. McVean, G., Awadalla, P., Fearnhead, P.: A coalescent-based method for detecting and estimating recombination from gene sequences. Genetics 160, 1231-1241 (2002)
70. Molenberghs, G., Geys, H.: Multivariate clustered data analysis in developmental toxicity studies. Stat. Neerl. 55, 319-345 (2001)
71. Molenberghs, G., Verbeke, G.: Models for Discrete Longitudinal Data. Springer, New York (2005)
72. Nelsen, R.B.: An Introduction to Copulas, 2nd edn. Springer, Berlin (2006)
73. Nott, D.J., Rydén, T.: Pairwise likelihood methods for inference in image models. Biometrika 86, 661-676 (1999)
74. Oakes, D., Ritz, J.: Regression in a bivariate copula model. Biometrika 87, 345-352 (2000)
75. Oman, S.D., Landsman, V., Carmel, Y., Kadmon, R.: Analyzing spatially distributed binary data using independent-block estimating equations. Biometrics 63, 892-900 (2007)
76. Parner, E.T.: A composite likelihood approach to multivariate survival data. Scand. J. Stat. 28, 295-302 (2001)
77. Parzen, M., Lipsitz, S.R., Fitzmaurice, G.M., Ibrahim, J.G., Troxel, A.: Pseudolikelihood methods for longitudinal binary data with non-ignorable missing responses and covariates. Stat. Med. 25, 2784-2796 (2006)
78. Parzen, M., Lipsitz, S.R., Fitzmaurice, G.M., Ibrahim, J.G., Troxel, A., Molenberghs, G.: Pseudo-likelihood methods for the analysis of longitudinal binary data subject to nonignorable non-monotone missingness. J. Data Sci. 5, 1-21 (2007)
79. Pinheiro, J.C., Bates, D.M.: Mixed-Effects Models in S and S-PLUS. Springer, New York (2000)
80. Plackett, R.L.: A class of bivariate distributions. J. Am. Stat. Assoc. 60, 516-522 (1965)
81. Poon, W.-Y., Lee, S.-Y.: Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficients. Psychometrika 52, 409-430 (1987) (correction in 53, p. 301)
82. Porcu, E., Mateu, J., Bevilacqua, M.: Covariance functions which are stationary or nonstazionary in space and stazionary in time. Stat. Neerl. 61, 358-382 (2007)
83. Renard, D., Geys, H., Molenberghs, G., Burzykowski, T., Buyse, M.: Validation of surrogate endpoints in multiple randomized clinical trials with discrete outcomes. Biom. J. 44, 921-935 (2002)
84. Renard, D., Molenberghs, G., Geys, H.: A pairwise likelihood approach to estimation in multilevel probit models. Comput. Stat. Data Anal. 44, 649-667 (2004)
85. Rodriguez, G., Goldman, N.: An assessment of estimation procedures for multilevel models with binary responses. J. R. Stat. Soc. Ser. A 158, 79-89 (1995)
86. Rýden, T.: Consistent and asymptotically normal parameter estimates for hidden Markov models. Ann. Stat. 22, 1841-1895 (1994)
87. Satterthwaites, F.E.: An approximate distribution of estimates of variance components. Biometrics 2, 110-114 (1946)
88. Song, P.X.-K.: Correlated Data Analysis: Modeling, Analytics and Applications. Springer, New York (2007)
89. Stein, M.L.: Approximating likelihoods for large spatial data sets. J. R. Stat. Soc. B 66, 275-296 (2004)
90. Stoyan, D., Kendall, W.S., Mecke, J.: Stochastic Geometry and its Applications, 2nd edn. Wiley, Chichester (1995)
91. Takeuchi, K.: Distribution of information statistics and criteria for adequacy of models. Math. Sci. 153, 12-18 (1976) (in Japanese)
92. Tibaldi, F., Molenberghs, G., Burzykowski, T., Geys, H.: Pseudolikelihood estimation for a marginal multivariate survival model. Stat. Med. 23, 947-963 (2004a)
93. Tibaldi, F., Barbosa, F.T., Molenberghs, G.: Modelling associations between time-to-event responses in pilot cancer clinical trials using a Plackett-Dale model. Stat. Med. 23, 2173-2186 (2004b)
94. Troxel, A.B., Lipsitz, S.R., Harrington, D.P.: Marginal models for the analysis of longitudinal measurements with noningnorable non-monotone missing data. Biometrika 85, 661-672 (1998)
95. Vandekerkhove, P.: Consistent and asymptotically normal parameter estimates for hidden Markov mixtures of Markov models. Bernoulli 11, 103-129 (2005)
96. Vardi, Y.: NetEork tomography: estimating source-destination traffic intensities from link data. J. Am. Stat. Assoc. 91, 365-377 (1996)
97. Varin, C., Czado, C.: Modeling pain severity diaries with mixed autoregressive ordinal probit models (2008, submitted)
98. Varin, C., Vidoni, P.: A note on composite likelihood inference and model selection. Biometrika 92, 519-528 (2005)
99. Varin, C., Vidoni, P.: Pairwise likelihood inference for ordinal categorical time series. Comput. Stat. Data Anal. 51, 2365-2373 (2006)
100. Varin, C., Vidoni, P.: Pairwise likelihood inference for general state space models. Econ. Rev. (2008, to appear)
101. Varin, C., Høst, G., Skare, Ø.: Pairwise likelihood inference in spatial generalized linear mixed models. Comput. Stat. Data Anal. 49, 1173-1191 (2005)
102. Warnes, J.J., Ripley, B.D.: Problems with likelihood estimation of covariance functions of spatial Gaussian processes. Biometrika 74, 640-642 (1987)
103. Wedderburn, R.W.M.: Quasi-likelihood functions, generalized linear models and the Gauss-Newton method. Biometrika 61, 439-447 (1974)
104. West, M., Harrison, J.: Bayesian Forecasting and Dynamic Models, 2nd edn. Springer, New York (1997)
105. Wilson, R.J., Nott, D.J.: Review of recent results on excursions set models. Image Anal. Stereol. 20, 71-78 (2001)
106. Zhang, H.: On estimation and prediction for spatial generalized linear mixed models. Biometrics 58, 129-136 (2002)
107. Zhao, L.P., Prentice, R.L.: Correlated binary regression using a quadratic exponential model. Biometrika 77, 642-648 (1990)
108. Zhao, Y., Joe, H.: Composite likelihood estimation in multivariate data analysis. Can. J. Stat. 33, 335-356 (2005)