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Statistica Sinica

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https://www.readbyqxmd.com/read/30344426/functional-linear-regression-models-for-nonignorable-missing-scalar-responses
#1
Tengfei Li, Fengchang Xie, Xiangnan Feng, Joseph G Ibrahim, Hongtu Zhu
As an important part of modern health care, medical imaging data, which can be regarded as densely sampled functional data, have been widely used for diagnosis, screening, treatment, and prognosis, such as finding breast cancer through mammograms. The aim of this paper is to propose a functional linear regression model for using functional (or imaging) predictors to predict clinical outcomes (e.g., disease status), while addressing missing clinical outcomes. We introduce an exponential tilting semiparametric model to account for the nonignorable missing data mechanism...
October 2018: Statistica Sinica
https://www.readbyqxmd.com/read/30294192/asymptotic-behavior-of-cox-s-partial-likelihood-and-its-application-to-variable-selection
#2
Runze Li, Jian-Jian Ren, Guangren Yang, Ye Yu
For theoretical properties of variable selection procedures for Cox's model, we study the asymptotic behavior of partial likelihood for the Cox model. We find that the partial likelihood does not behave like an ordinary likelihood, whose sample average typically tends to its expected value, a finite number, in probability. Under some mild conditions, we prove that the sample average of partial likelihood tends to infinity at the rate of the logarithm of the sample size, in probability. We apply the asymptotic results on the partial likelihood to study tuning parameter selection for penalized partial likelihood...
October 2018: Statistica Sinica
https://www.readbyqxmd.com/read/30283213/a-mean-score-method-for-sensitivity-analysis-to-departures-from-the-missing-at-random-assumption-in-randomised-trials
#3
Ian R White, James Carpenter, Nicholas J Horton
Most analyses of randomised trials with incomplete outcomes make untestable assumptions and should therefore be subjected to sensitivity analyses. However, methods for sensitivity analyses are not widely used. We propose a mean score approach for exploring global sensitivity to departures from missing at random or other assumptions about incomplete outcome data in a randomised trial. We assume a single outcome analysed under a generalised linear model. One or more sensitivity parameters, specified by the user, measure the degree of departure from missing at random in a pattern mixture model...
October 2018: Statistica Sinica
https://www.readbyqxmd.com/read/30135619/on-estimation-of-the-optimal-treatment-regime-with-the-additive-hazards-model
#4
Suhyun Kang, Wenbin Lu, Jiajia Zhang
We propose a doubly robust estimation method for the optimal treatment regime based on an additive hazards model with censored survival data. Specifically, we introduce a new semiparametric additive hazard model which allows flexible baseline covariate effects in the control group and incorporates marginal treatment effect and its linear interaction with covariates. In addition, we propose a time-dependent propensity score to construct an A-learning type of estimating equations. The resulting estimator is shown to be consistent and asymptotically normal when either the baseline effect model for covariates or the propensity score is correctly specified...
July 2018: Statistica Sinica
https://www.readbyqxmd.com/read/29643721/scalable-bayesian-variable-selection-using-nonlocal-prior-densities-in-ultrahigh-dimensional-settings
#5
Minsuk Shin, Anirban Bhattacharya, Valen E Johnson
Bayesian model selection procedures based on nonlocal alternative prior densities are extended to ultrahigh dimensional settings and compared to other variable selection procedures using precision-recall curves. Variable selection procedures included in these comparisons include methods based on g -priors, reciprocal lasso, adaptive lasso, scad, and minimax concave penalty criteria. The use of precision-recall curves eliminates the sensitivity of our conclusions to the choice of tuning parameters. We find that Bayesian variable selection procedures based on nonlocal priors are competitive to all other procedures in a range of simulation scenarios, and we subsequently explain this favorable performance through a theoretical examination of their consistency properties...
April 2018: Statistica Sinica
https://www.readbyqxmd.com/read/29422761/modeling-subject-specific-nonautonomous-dynamics
#6
Siyuan Zhou, Debashis Paul, Jie Peng
We consider modeling non-autonomous dynamical systems for a group of subjects. The proposed model involves a common baseline gradient function and a multiplicative time-dependent subject-specific effect that accounts for phase and amplitude variations in the rate of change across subjects. The baseline gradient function is represented in a spline basis and the subject-specific effect is modeled as a polynomial in time with random coefficients. We establish appropriate identifiability conditions and propose an estimator based on the hierarchical likelihood...
January 2018: Statistica Sinica
https://www.readbyqxmd.com/read/29386856/two-sample-tests-for-high-dimensional-linear-regression-with-an-application-to-detecting-interactions
#7
Yin Xia, Tianxi Cai, T Tony Cai
Motivated by applications in genomics, we consider in this paper global and multiple testing for the comparisons of two high-dimensional linear regression models. A procedure for testing the equality of the two regression vectors globally is proposed and shown to be particularly powerful against sparse alternatives. We then introduce a multiple testing procedure for identifying unequal coordinates while controlling the false discovery rate and false discovery proportion. Theoretical justifications are provided to guarantee the validity of the proposed tests and optimality results are established under sparsity assumptions on the regression coefficients...
January 2018: Statistica Sinica
https://www.readbyqxmd.com/read/29097879/predicting-disease-risk-by-transformation-models-in-the-presence-of-unspecified-subgroup-membership
#8
Qianqian Wang, Yanyuan Ma, Yuanjia Wang
Some biomedical studies lead to mixture data. When a discrete covariate defining subgroup membership is missing for some of the subjects in a study, the distribution of the outcome follows a mixture distribution of the subgroup-specific distributions. Taking into account the uncertain distribution of the group membership and the covariates, we model the relation between the disease onset time and the covariates through transformation models in each sub-population, and develop a nonparametric maximum likelihood based estimation implemented through EM algorithm along with its inference procedure...
October 2017: Statistica Sinica
https://www.readbyqxmd.com/read/28959115/semiparametric-regression-analysis-of-repeated-current-status-data
#9
Baosheng Liang, Xingwei Tong, Donglin Zeng, Yuanjia Wang
In many clinical studies, patients may be asked to report their medication adherence, presence of side effects, substance use, and hospitalization information during the study period. However, the exact occurrence time of these recurrent events may not be available due to privacy protection, recall difficulty, or incomplete medical records. Instead, the only available information is whether the events of interest have occurred during the past period. In this paper, we call these incomplete recurrent events as repeated current status data...
July 2017: Statistica Sinica
https://www.readbyqxmd.com/read/28663685/variable-selection-via-partial-correlation
#10
Runze Li, Jingyuan Liu, Lejia Lou
Partial correlation based variable selection method was proposed for normal linear regression models by Bühlmann, Kalisch and Maathuis (2010) as a comparable alternative method to regularization methods for variable selection. This paper addresses two important issues related to partial correlation based variable selection method: (a) whether this method is sensitive to normality assumption, and (b) whether this method is valid when the dimension of predictor increases in an exponential rate of the sample size...
July 2017: Statistica Sinica
https://www.readbyqxmd.com/read/28649172/control-function-assisted-ipw-estimation-with-a-secondary-outcome-in-case-control-studies
#11
Tamar Sofer, Marilyn C Cornelis, Peter Kraft, Eric J Tchetgen Tchetgen
Case-control studies are designed towards studying associations between risk factors and a single, primary outcome. Information about additional, secondary outcomes is also collected, but association studies targeting such secondary outcomes should account for the case-control sampling scheme, or otherwise results may be biased. Often, one uses inverse probability weighted (IPW) estimators to estimate population effects in such studies. IPW estimators are robust, as they only require correct specification of the mean regression model of the secondary outcome on covariates, and knowledge of the disease prevalence...
April 2017: Statistica Sinica
https://www.readbyqxmd.com/read/28018116/the-statistics-and-mathematics-of-high-dimension-low-sample-size-asymptotics
#12
Dan Shen, Haipeng Shen, Hongtu Zhu, J S Marron
The aim of this paper is to establish several deep theoretical properties of principal component analysis for multiple-component spike covariance models. Our new results reveal an asymptotic conical structure in critical sample eigendirections under the spike models with distinguishable (or indistinguishable) eigenvalues, when the sample size and/or the number of variables (or dimension) tend to infinity. The consistency of the sample eigenvectors relative to their population counterparts is determined by the ratio between the dimension and the product of the sample size with the spike size...
October 2016: Statistica Sinica
https://www.readbyqxmd.com/read/27667908/time-varying-coefficient-models-for-joint-modeling-binary-and-continuous-outcomes-in-longitudinal-data
#13
Esra Kürüm, Runze Li, Saul Shiffman, Weixin Yao
Motivated by an empirical analysis of ecological momentary assessment data (EMA) collected in a smoking cessation study, we propose a joint modeling technique for estimating the time-varying association between two intensively measured longitudinal responses: a continuous one and a binary one. A major challenge in joint modeling these responses is the lack of a multivariate distribution. We suggest introducing a normal latent variable underlying the binary response and factorizing the model into two components: a marginal model for the continuous response, and a conditional model for the binary response given the continuous response...
July 2016: Statistica Sinica
https://www.readbyqxmd.com/read/27667907/partial-linear-varying-multi-index-coefficient-model-for-integrative-gene-environment-interactions
#14
Xu Liu, Yuehua Cui, Runze Li
Gene-environment (G×E) interactions play key roles in many complex diseases. An increasing number of epidemiological studies have shown the combined effect of multiple environmental exposures on disease risk. However, no appropriate statistical models have been developed to conduct a rigorous assessment of such combined effects when G×E interactions are considered. In this paper, we propose a partial linear varying multi-index coefficient model (PLVMICM) to assess how multiple environmental factors act jointly to modify individual genetic risk on complex disease...
July 2016: Statistica Sinica
https://www.readbyqxmd.com/read/28316451/joint-estimation-of-multiple-high-dimensional-precision-matrices
#15
T Tony Cai, Hongzhe Li, Weidong Liu, Jichun Xie
Motivated by analysis of gene expression data measured in different tissues or disease states, we consider joint estimation of multiple precision matrices to effectively utilize the partially shared graphical structures of the corresponding graphs. The procedure is based on a weighted constrained ℓ∞/ℓ1 minimization, which can be effectively implemented by a second-order cone programming. Compared to separate estimation methods, the proposed joint estimation method leads to estimators converging to the true precision matrices faster...
April 2016: Statistica Sinica
https://www.readbyqxmd.com/read/27540275/joint-structure-selection-and-estimation-in-the-time-varying-coefficient-cox-model
#16
Wei Xiao, Wenbin Lu, Hao Helen Zhang
Time-varying coefficient Cox model has been widely studied and popularly used in survival data analysis due to its flexibility for modeling covariate effects. It is of great practical interest to accurately identify the structure of covariate effects in a time-varying coefficient Cox model, i.e. covariates with null effect, constant effect and truly time-varying effect, and estimate the corresponding regression coefficients. Combining the ideas of local polynomial smoothing and group nonnegative garrote, we develop a new penalization approach to achieve such goals...
April 2016: Statistica Sinica
https://www.readbyqxmd.com/read/27158214/prediction-based-termination-rule-for-greedy-learning-with-massive-data
#17
Chen Xu, Shaobo Lin, Jian Fang, Runze Li
The appearance of massive data has become increasingly common in contemporary scientific research. When sample size n is huge, classical learning methods become computationally costly for the regression purpose. Recently, the orthogonal greedy algorithm (OGA) has been revitalized as an efficient alternative in the context of kernel-based statistical learning. In a learning problem, accurate and fast prediction is often of interest. This makes an appropriate termination crucial for OGA. In this paper, we propose a new termination rule for OGA via investigating its predictive performance...
April 2016: Statistica Sinica
https://www.readbyqxmd.com/read/27057128/marginal-structural-cox-models-with-case-cohort-sampling
#18
Hana Lee, Michael G Hudgens, Jianwen Cai, Stephen R Cole
A common objective of biomedical cohort studies is assessing the effect of a time-varying treatment or exposure on a survival time. In the presence of time-varying confounders, marginal structural models fit using inverse probability weighting can be employed to obtain a consistent and asymptotically normal estimator of the causal effect of a time-varying treatment. This article considers estimation of parameters in the semiparametric marginal structural Cox model (MSCM) from a case-cohort study. Case-cohort sampling entails assembling covariate histories only for cases and a random subcohort, which can be cost effective, particularly in large cohort studies with low outcome rates...
April 2016: Statistica Sinica
https://www.readbyqxmd.com/read/27418749/feature-screening-in-ultrahigh-dimensional-cox-s-model
#19
Guangren Yang, Ye Yu, Runze Li, Anne Buu
Survival data with ultrahigh dimensional covariates such as genetic markers have been collected in medical studies and other fields. In this work, we propose a feature screening procedure for the Cox model with ultrahigh dimensional covariates. The proposed procedure is distinguished from the existing sure independence screening (SIS) procedures (Fan, Feng and Wu, 2010, Zhao and Li, 2012) in that the proposed procedure is based on joint likelihood of potential active predictors, and therefore is not a marginal screening procedure...
2016: Statistica Sinica
https://www.readbyqxmd.com/read/26941542/regularized-quantile-regression-and-robust-feature-screening-for-single-index-models
#20
Wei Zhong, Liping Zhu, Runze Li, Hengjian Cui
We propose both a penalized quantile regression and an independence screening procedure to identify important covariates and to exclude unimportant ones for a general class of ultrahigh dimensional single-index models, in which the conditional distribution of the response depends on the covariates via a single-index structure. We observe that the linear quantile regression yields a consistent estimator of the direction of the index parameter in the single-index model. Such an observation dramatically reduces computational complexity in selecting important covariates in the single-index model...
January 2016: Statistica Sinica
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