This proposal reflects our continuing interest in solving problems of measurement error, incomplete data nd/or functional (curve) data in general regression settings. The proposed research topics have arisen aturally from various important studies. These studies include (i) a colon cancer tumorigenesis project, vhich consists of a series of experiments to study .colon cancer tumorigenesis at the cellular level, (ii) artially degraded mRNA fecal microarray experiments, in which the investigators intend to recover genetic nformation from exfoliated colonocytes in the fecal stream as the initial step in building a non-invasive colon .ancer detection tool, and (iii) a spectroscopic oblique incidence reflectometry skin-lesion diagnostic study, vhich aims to use a newly developed bioengineering device to identify physiological features that reflect the athology of skin lesions. """"""""he statistical methods we investigate can be divided into five primary research goals: . To develop and evaluate marginal estimation methods under general nonparametric and semiparametric egression models with flexible data collection designs. 2. To develop and evaluate semiparametric methods when a major covariate is a latent variable in a secondary mixed model. 3. To develop and evaluate efficient semiparametric methods to accommodate incomplete data using estimating equation and imputation-maximization approaches. 4. To develop and evaluate nonparametric and semiparametric generalized linear model approaches for;orrelated data with functional covariates and a scalar response. 5. To develop and evaluate semiparametric standardization procedures for microarray data with a large proportion of outlying observations. The major focus of this proposal is the development of intuitive, efficient and computationally feasible methods without imposing unnecessary parametric assumptions. We expect our effort on these interesting biological studies to have significant impact on advancements in cancer research.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Special Emphasis Panel (ZRG1-HOP-T (03))
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Dunn, Michelle C
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University of Michigan Ann Arbor
Biostatistics & Other Math Sci
Schools of Arts and Sciences
Ann Arbor
United States
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Li, Yun; Zhu, Ji; Wang, Naisyin (2015) Regularized Semiparametric Estimation for Ordinary Differential Equations. Technometrics 57:341-350
Jiang, Bei; Wang, Naisyin; Sammel, Mary D et al. (2015) Modeling Short- and Long-Term Characteristics of Follicle Stimulating Hormone as Predictors of Severe Hot Flashes in Penn Ovarian Aging Study. J R Stat Soc Ser C Appl Stat 64:731-753
Jiang, Bei; Elliott, Michael R; Sammel, Mary D et al. (2015) Joint modeling of cross-sectional health outcomes and longitudinal predictors via mixtures of means and variances. Biometrics 71:487-97
Mukherjee, A; Chen, K; Wang, N et al. (2015) On the degrees of freedom of reduced-rank estimators in multivariate regression. Biometrika 102:457-477
Jiang, Bei; Sammel, Mary D; Freeman, Ellen W et al. (2015) Bayesian estimation of associations between identified longitudinal hormone subgroups and age at final menstrual period. BMC Med Res Methodol 15:106
Hu, Zonghui; Follmann, Dean A; Wang, Naisyin (2014) Estimation of mean response via effective balancing score. Biometrika 101:613-624
Li, Yehua; Wang, Naisyin; Carroll, Raymond J (2013) Selecting the Number of Principal Components in Functional Data. J Am Stat Assoc 108:
Zhou, Jianhui; Wang, Nae-Yuh; Wang, Naisyin (2013) Functional Linear Model with Zero-value Coefficient Function at Sub-regions. Stat Sin 23:25-50
Cho, Youngmi; Kim, Hyemee; Turner, Nancy D et al. (2011) A chemoprotective fish oil- and pectin-containing diet temporally alters gene expression profiles in exfoliated rat colonocytes throughout oncogenesis. J Nutr 141:1029-35
Kuskie, Kyle R; Smith, Jacqueline L; Wang, Naisyin et al. (2011) Effects of location for collection of air samples on a farm and time of day of sample collection on airborne concentrations of virulent Rhodococcus equi at two horse breeding farms. Am J Vet Res 72:73-9

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