Bayesian estimation of varying-coefficient models with missing data, with application to the Singapore Longitudinal Aging Study

References

• Sahadevan S, Tan NJ, Tan T, Tan S. Cognitive testing of elderly Chinese people in Singapore: influence of education and age on normative scores. Age Age. 1997;26(6):481–486. doi: 10.1093/ageing/26.6.481
   PubMed Web of Science ®Google Scholar
• Zhang ZX, Zahner GE, Román GC, Liu J, Hong Z, Qu QM, Liu XH, Zhang XJ, Zhou B, Wu CB, Tang MN, Hong X, Li H. Dementia subtypes in China: prevalence in Beijing, Xian, Shanghai, and Chengdu. Arch Neurol. 2005;62(3):447–453. doi: 10.1001/archneur.62.3.447
   PubMed Web of Science ®Google Scholar
• Feng L, Chong MS, Lim WS, Ng TP. The modified Mini-Mental State Examination test: normative data for Singapore Chinese older adults and its performance in detecting early cognitive impairment. Singapore Med J. 2012;53(7):458–462.
   PubMed Web of Science ®Google Scholar
• Fan J, Yao Q, Cai Z. Adaptive varying-coefficient linear models. J R Stat Soc: Ser B (Stat Methodol). 2003;65(1):57–80. doi: 10.1111/1467-9868.00372
   Google Scholar
• Xia Y, Zhang W, Tong H. Efficient estimation for semivarying-coefficient models. Biometrika. 2004;91(3):661–681. doi: 10.1093/biomet/91.3.661
   Web of Science ®Google Scholar
• Wang L, Li H, Huang JZ. Variable selection in nonparametric varying-coefficient models for analysis of repeated measurements. J Am Stat Assoc. 2008;103(484):1556–1569. doi: 10.1198/016214508000000788
   PubMed Web of Science ®Google Scholar
• Lu Z, Steinskog DJ, Tjøstheim D, Yao Q. Adaptively varying-coefficient spatiotemporal models. J R Stat Soc: Ser B (Stat Methodol). 2009;71(4):859–880. doi: 10.1111/j.1467-9868.2009.00710.x
   Google Scholar
• Hoover DR, Rice JA, Wu CO, Yang L-P. Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data. Biometrika. 1998;85(4):809–822. doi: 10.1093/biomet/85.4.809
   Web of Science ®Google Scholar
• Li J, Wong WK. A semi-parametric analysis for identifying scleroderma patients responsive to an anti-fibrotic agent. Contemp Clin Trials. 2009;30(2):105–113. doi: 10.1016/j.cct.2008.10.003
   PubMed Web of Science ®Google Scholar
• Li J, Palta M. Bandwidth selection through cross-validation for semi-parametric varying-coefficient partially linear models. J Stat Comput Simul. 2009;79(11):1277–1286. doi: 10.1080/00949650802260071
   Web of Science ®Google Scholar
• Li J, Xia Y, Palta M, Shankar A. Impact of unknown covariance structures in semiparametric models for longitudinal data: an application to wisconsin diabetes data. Comput Stat Data Anal. 2009;53(12):4186–4197. doi: 10.1016/j.csda.2009.05.008
   Web of Science ®Google Scholar
• Fan J, Zhang W. Statistical estimation in varying coefficient models. Ann Stat. 1999;27(5):1491–1518. doi: 10.1214/aos/1017939139
   Web of Science ®Google Scholar
• Cheng M, Zhang W, Chen L. Statistical estimation in generalized multiparameter likelihood models. J Am Stat Assoc. 2009;104(487):1179–1191. doi: 10.1198/jasa.2009.tm08430
   Web of Science ®Google Scholar
• Zhang W. Identification of the constant components in generalised semivarying coefficient models by cross-validation. Stat Sin. 2011;21(4):1913–1929.
   Web of Science ®Google Scholar
• Li J, Lee MLT. Analysis of failure time using threshold regression with semi-parametric varying coefficients. Stat Neerlandica. 2011;65:164–182. doi: 10.1111/j.1467-9574.2011.00481.x
   Web of Science ®Google Scholar
• Li J, Zhang W. A semiparametric threshold model for censored longitudinal data analysis. J Am Stat Assoc. 2011;106:685–696. doi: 10.1198/jasa.2011.tm10592
   Web of Science ®Google Scholar
• Biller C, Fahrmeir L. Bayesian varying-coefficient models using adaptive regression splines. Stat Model. 2001;1(3):195–211. doi: 10.1191/147108201128168
   Google Scholar
• Reich BJ, Fuentes M, Herring AH, Evenson KR. Bayesian variable selection for multivariate spatially varying coefficient regression. Biometrics. 2010;66(3):772–782. doi: 10.1111/j.1541-0420.2009.01333.x
   PubMed Web of Science ®Google Scholar
• Lang S, Brezger A. Bayesian p-splines. J Comput Graph Stat. 2004;13(1):183–212. doi: 10.1198/1061860043010
   Web of Science ®Google Scholar
• Nott DJ, Li J. A sign based loss approach to model selection in nonparametric regression. Stat Comput. 2010;20(4):485–498. doi: 10.1007/s11222-009-9139-6
   Web of Science ®Google Scholar
• Shi JQ, Wang B, Will EJ, West RM. Mixed-effects Gaussian process functional regression models with application to dose–response curve prediction. Stat Med. 2012;31(26):3165–3177. doi: 10.1002/sim.4502
   PubMed Web of Science ®Google Scholar
• Little RJ, Rubin DB. Statistical analysis with missing data. Hoboken, NJ: Wiley; 2002.
   Google Scholar
• Tanner MA, Wong WH. The calculation of posterior distributions by data augmentation. J Am Stat Assoc. 1987;82(398):528–540. doi: 10.1080/01621459.1987.10478458
   Web of Science ®Google Scholar
• Wu H, Zhang J.-T. Nonparametric regression methods for longitudinal data analysis: mixed-effects modeling approaches. Hoboken, NJ: Wiley-Interscience; 2006.
   Google Scholar
• Ruppert D, Wand MP, Carroll RJ. Semiparametric regression. Cambridge: Cambridge University Press; 2003.
   Google Scholar
• Gelman A. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Anal. 2006;1(3):515–534.
   Web of Science ®Google Scholar
• Huang A, Wand MP. Simple marginally noninformative prior distributions for covariance matrices. Bayesian Anal. 2013;8:439–452. doi: 10.1214/13-BA815
   Web of Science ®Google Scholar
• Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian data analysis. Boca Raton, FL: Chapman & Hall/CRC; 2004.
   Google Scholar
• Devroye L. Sample-based non-uniform random variate generation. Proceedings of the 18th conference on winter simulation, ACM; 1986. p. 260–265.
   Google Scholar
• Andrews DF, Mallows CL. Scale mixtures of normal distributions. J R Stat Soc Ser B (Methodol). 1974;36(1):99–102.
   Web of Science ®Google Scholar
• Holmes CC, Held L. Bayesian auxiliary variable models for binary and multinomial regression. Bayesian Anal. 2006;1(1):145–168. doi: 10.1214/06-BA105
   Web of Science ®Google Scholar
• Kim J, Basak J, Holtzman D. The role of apolipoprotein e in Alzheimer's disease. Neuron. 2009;63:287–303. doi: 10.1016/j.neuron.2009.06.026
   PubMed Web of Science ®Google Scholar
• Feng L, Li J, Yap K, Kua E, Ng TP. Vitamin b-12, apolipoprotein e genotype and cognitive performance in community-living older adults: evidence of gene-micronutrient interaction. Am J Clin Nutr. 2009;89:1263–1268. doi: 10.3945/ajcn.2008.26969
   PubMed Web of Science ®Google Scholar