Sequences of internal transcribed spacers (ITS) of nuclear ribosomal DNA, the chloroplast
ndhF gene, and chloroplast
trnL-F regions (
trnL intron, and
trnL UAA 3′ exon-
trnF GAA intergenic spacer) ...were used for phylogenetic analyses of
Rhus, a genus disjunctly distributed in Asia, Europe, Hawaii, North America, and Northern Central America. Both ITS and cpDNA data sets support the monophyly of
Rhus. The monophyly of subgenus
Rhus was suggested by the combined cpDNA and ITS data, and largely supported in the cpDNA data except that
Rhus microphylla of subgenus
Lobadium was nested within it. The monophyly of subgenus
Lobadium was strongly supported in the ITS data, whereas the cpDNA data revealed two main clades within the subgenus, which formed a trichotomy with the clade of subgenus
Rhus plus
R. microphylla. The ITS and cpDNA trees differ in the positions of
Rhus michauxii,
R. microphylla, and
Rhus rubifolia, and hybridization may have caused this discordance. Fossil evidence indicates that
Rhus dates back to the early Eocene. The penalized likelihood method was used to estimate divergence times, with fossils of
Rhus subgenus
Lobadium,
Pistacia and
Toxicodendron used for age constraints.
Rhus diverged from its closest relative at 49.1
±
2.1 million years ago (Ma), the split of subgenus
Lobadium and subgenus
Rhus was at 38.1
±
3.0 Ma.
Rhus most likely migrated from North America into Asia via the Bering Land Bridge during the Late Eocene (33.8
±
3.1 Ma).
Rhus coriaria from southern Europe and western Asia diverged from its relatives in eastern Asia at 24.4
±
3.2 Ma. The Hawaiian
Rhus sandwicensis diverged from the Asian
Rhus chinensis at 13.5
±
3.0 Ma. Subgenus
Lobadium was inferred to be of North American origin. Taxa of subgenus
Lobadium then migrated southward to Central America. Furthermore, we herein make the following three nomenclatural combinations: (1)
Searsia leptodictya (Diels) T. S. Yi, A. J. Miller and J. Wen, comb. nov., (2)
Searsia pyroides (A. Rich.) T. S. Yi, A. J. Miller and J. Wen, comb. nov., and (3)
Searsia undulata (Jacq.) T. S. Yi, A. J. Miller and J. Wen, because our analyses support the segregation of
Searsia from
Rhus.
The penalized likelihood approach of Fan and Li (
2001
,
2002
) differs from the traditional variable selection procedures in that it deletes the non-significant variables by estimating their ...coefficients as zero. Nevertheless, the desirable performance of this shrinkage methodology relies heavily on an appropriate selection of the tuning parameter which is involved in the penalty functions. In this work, new estimates of the norm of the error are firstly proposed through the use of Kantorovich inequalities and, subsequently, applied to the frailty models framework. These estimates are used in order to derive a tuning parameter selection procedure for penalized frailty models and clustered data. In contrast with the standard methods, the proposed approach does not depend on resampling and therefore results in a considerable gain in computational time. Moreover, it produces improved results. Simulation studies are presented to support theoretical findings and two real medical data sets are analyzed.
Key Clinical Message
FDG PET‐CT is a useful imaging tool in the diagnosis and response assessment of neurolymphomatosis, especially in cases of otherwise unexplained neuropathy following conventional ...diagnostic work‐up including lumbar puncture, CT, and MRI. The use of a novel PET reconstruction algorithm improves image quality and lesion detection through increased signal‐to‐noise ratio.
FDG PET‐CT is a useful imaging tool in the diagnosis and response assessment of neurolymphomatosis, especially in cases of otherwise unexplained neuropathy following conventional diagnostic work‐up including lumbar puncture, CT, and MRI. The use of a novel PET reconstruction algorithm improves image quality and lesion detection through increased signal‐to‐noise ratio.
We consider inference for a semiparametric stochastic mixed model for longitudinal data. This model uses parametric fixed effects to represent the covariate effects and an arbitrary smooth function ...to model the time effect and accounts for the within-subject correlation using random effects and a stationary or nonstationary stochastic process. We derive maximum penalized likelihood estimators of the regression coefficients and the nonparametric function. The resulting estimator of the nonparametric function is a smoothing spline. We propose and compare frequentist inference and Bayesian inference on these model components. We use restricted maximum likelihood to estimate the smoothing parameter and the variance components simultaneously. We show that estimation of all model components of interest can proceed by fitting a modified linear mixed model. We illustrate the proposed method by analyzing a hormone dataset and evaluate its performance through simulations.
It is well known that non-small cell lung cancer (NSCLC) is a heterogeneous group of diseases. Previous studies have demonstrated genetic variation among different ethnic groups in the epidermal ...growth factor receptor (EGFR) in NSCLC. Research by our group and others has recently shown a lower frequency of EGFR mutations in African Americans with NSCLC, as compared to their White counterparts. In this study, we use our original study data of EGFR pathway genetics in African American NSCLC as an example to illustrate that univariate analyses based on aggregation versus partition of data leads to contradictory results, in order to emphasize the importance of controlling statistical confounding. We further investigate analytic approaches in logistic regression for data with separation, as is the case in our example data set, and apply appropriate methods to identify predictors of EGFR mutation. Our simulation shows that with separated or nearly separated data, penalized maximum likelihood (PML) produces estimates with smallest bias and approximately maintains the nominal value with statistical power equal to or better than that from maximum likelihood and exact conditional likelihood methods. Application of the PML method in our example data set shows that race and EGFR-FISH are independently significant predictors of EGFR mutation.
In many biomedical studies, subjects may experience the outcome of interest more than once over a period of observation; outcomes of this sort have been termed recurrent events. A model that is ...becoming increasingly popular for modeling association between recurrent survival times is the use of a frailty model. In recent years a number of papers appeared, extending the survival models to models that are suitable to handle more complex survival data as recurrent events. We present here frailty model extensions to analyze recurrent events data: cure frailty models for a mixture of susceptible and insusceptible subjects for the event of interest; nested frailty models when data are clustered at several hierarchical levels and joint frailty models for the joint analysis of recurrent events and death. We performed a semi-parametric penalized likelihood approach to estimate the different parameters. Those different models can be fitted using the R package “frailtypack”.
Ordered categorial predictors are a common case in regression modelling. In contrast to the case of ordinal response variables, ordinal predictors have been largely neglected in the literature. In ...this paper, existing methods are reviewed and the use of penalized regression techniques is proposed. Based on dummy coding two types of penalization are explicitly developed; the first imposes a difference penalty, the second is a ridge type refitting procedure. Also a Bayesian motivation is provided. The concept is generalized to the case of non-normal outcomes within the framework of generalized linear models by applying penalized likelihood estimation. Simulation studies and real world data serve for illustration and to compare the approaches to methods often seen in practice, namely simple linear regression on the group labels and pure dummy coding. Especially the proposed difference penalty turns out to be highly competitive. Les variables indépendantes catégoriques ordinales sont un cas courant dans les modèles de régression. Contraire-ment au cas des variables dépendantes ordinales, les variables independantes ordinales ont été largement négligées par la recherche. Le présent article présente les méthodes existantes et propose l'utilisation de techniques de régression pénalisée. Deux types de pénalisation basés sur des variables dummy sont exposés; le premier impose une pénalité de différence, le second est une procédure basée sur une forme de régression ridge. D'autre part, une motivation baysienne est présentée. La méthode est également appliquée au cas de variables dépendantes non gaussiennes. Des études de simulation et des données réelles servent à illustrer et à comparer les nouvelles méthodes aux méthodes que Ton rencontre souvent dans la pratique - à savoir les régressions linéaires sur les nombres entiers et sur des variables dummy sans penalité. Une pénalite de différence notamment a montré de bons résultats.
This article investigates unsupervised classification techniques for categorical multivariate data. The study employs multivariate multinomial mixture modeling, which is a type of model particularly ...applicable to multilocus genotypic data. A model selection procedure is used to simultaneously select the number of components and the relevant variables. A non-asymptotic oracle inequality is obtained, leading to the proposal of a new penalized maximum likelihood criterion. The selected model proves to be asymptotically consistent under weak assumptions on the true probability underlying the observations. The main theoretical result obtained in this study suggests a penalty function defined to within a multiplicative parameter. In practice, the data-driven calibration of the penalty function is made possible by slope heuristics. Based on simulated data, this procedure is found to improve the performance of the selection procedure with respect to classical criteria such as BIC and AIC. The new criterion provides an answer to the question "Which criterion for which sample size?" Examples of real dataset applications are also provided.
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