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  1. AU="Ruppert, David"
  2. AU="Ochoa, Ayako Miyashita"
  3. AU=Hemphill H E
  4. AU="Piton, Yasmin Vendruscolo"
  5. AU="Thadani, Nicole N"
  6. AU="Allison E. Norlander" AU="Allison E. Norlander"
  7. AU="Wei, Peifa"
  8. AU="Colak, Errol"
  9. AU="Hassan, Hosni M"
  10. AU="Becker, D T"
  11. AU="Lewis-Lloyd, Christopher A"
  12. AU=Doshi Rupali Kotwal AU=Doshi Rupali Kotwal
  13. AU="Kore Schlottau"
  14. AU="Jamjoom, Mona"
  15. AU="Bergmann, P."
  16. AU="Li, Congju"
  17. AU="Christenson, Robert"
  18. AU="O'Neill, Hugh M"
  19. AU="Vaccaro, Daniel H."
  20. AU=Panchin Yuri V
  21. AU=McIlvennan Colleen K
  22. AU="Khan, Mohammad Afsar"
  23. AU="Zhuang, Hua-Lu"
  24. AU="Lopez, Omar M"
  25. AU=Asfaw Abay
  26. AU="Santana, Ricardo Costa de"
  27. AU="Ortega, Johis"
  28. AU="Sogaolu, Moyosore"
  29. AU="Duchesne, Gabriela"
  30. AU="Sander, Klaus"
  31. AU="Wiegersma, Aline Marileen"
  32. AU=Mehta Yatin
  33. AU="Ki Hwan Kim"
  34. AU="Gulati, Rajiv"
  35. AU="Sullivan, Christopher"
  36. AU="Meier-Stephenson, Vanessa C"
  37. AU=Kim Joo Seop
  38. AU="Mortensen, Jennifer L"
  39. AU="Manthey, Helga D"
  40. AU="Baker, Susan"
  41. AU="Gunasegaram, James R"
  42. AU="Jung, Steffen"
  43. AU="Cairns, Anita"
  44. AU="Fox, Lindsay"

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  1. Artikel ; Online: Bootstrap Inference for Quantile-based Modal Regression

    Zhang, Tao / Kato, Kengo / Ruppert, David

    Journal of the American Statistical Association. 2023 Jan. 2, v. 118, no. 541 p.122-134

    2023  

    Abstract: In this article, we develop uniform inference methods for the conditional mode based on quantile regression. Specifically, we propose to estimate the conditional mode by minimizing the derivative of the estimated conditional quantile function defined by ... ...

    Abstract In this article, we develop uniform inference methods for the conditional mode based on quantile regression. Specifically, we propose to estimate the conditional mode by minimizing the derivative of the estimated conditional quantile function defined by smoothing the linear quantile regression estimator, and develop two bootstrap methods, a novel pivotal bootstrap and the nonparametric bootstrap, for our conditional mode estimator. Building on high-dimensional Gaussian approximation techniques, we establish the validity of simultaneous confidence rectangles constructed from the two bootstrap methods for the conditional mode. We also extend the preceding analysis to the case where the dimension of the covariate vector is increasing with the sample size. Finally, we conduct simulation experiments and a real data analysis using the U.S. wage data to demonstrate the finite sample performance of our inference method. The supplemental materials include the wage dataset, R codes and an appendix containing proofs of the main results, additional simulation results, discussion of model misspecification and quantile crossing, and additional details of the numerical implementation.
    Schlagwörter crossing ; data collection ; regression analysis ; sample size ; High-dimensional CLT ; Kernel smoothing ; Modal regression ; Pivotal bootstrap ; Quantile regression
    Sprache Englisch
    Erscheinungsverlauf 2023-0102
    Umfang p. 122-134.
    Erscheinungsort Taylor & Francis
    Dokumenttyp Artikel ; Online
    ZDB-ID 2064981-2
    ISSN 1537-274X
    ISSN 1537-274X
    DOI 10.1080/01621459.2021.1918130
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  2. Artikel: Density estimation on a network

    Liu, Yang / Ruppert, David

    Computational statistics & data analysis. 2021 Apr., v. 156

    2021  

    Abstract: A novel approach is proposed for density estimation on a network. Nonparametric density estimation on a network is formulated as a nonparametric regression problem by binning. Nonparametric regression using local polynomial kernel-weighted least squares ... ...

    Abstract A novel approach is proposed for density estimation on a network. Nonparametric density estimation on a network is formulated as a nonparametric regression problem by binning. Nonparametric regression using local polynomial kernel-weighted least squares have been studied rigorously, and its asymptotic properties make it superior to kernel estimators such as the Nadaraya–Watson estimator. When applied to a network, the best estimator near a vertex depends on the amount of smoothness at the vertex. Often, there are no compelling reasons to assume that a density will be continuous or discontinuous at a vertex, hence a data driven approach is proposed. To estimate the density in a neighborhood of a vertex, a two-step procedure is proposed. The first step of this pretest estimator fits a separate local polynomial regression on each edge using data only on that edge, and then tests for equality of the estimates at the vertex. If the null hypothesis is not rejected, then the second step re-estimates the regression function in a small neighborhood of the vertex, subject to a joint equality constraint. Since the derivative of the density may be discontinuous at the vertex, a piecewise polynomial local regression estimate is used to model the change in slope. The special case of local piecewise linear regression is studied in detail and the leading bias and variance terms are derived using weighted least squares theory. The proposed approach will remove the bias near a vertex that has been noted for existing methods, which typically do not allow for discontinuity at vertices. For a fixed network, the proposed method scales sub-linearly with sample size and it can be extended to regression and varying coefficient models on a network. The working of the proposed model is demonstrated by simulation studies and applications to a dendrite network dataset.
    Schlagwörter bias ; data analysis ; data collection ; density ; estimation ; least squares ; models ; sample size ; seeds ; testing ; variance
    Sprache Englisch
    Erscheinungsverlauf 2021-04
    Erscheinungsort Elsevier B.V.
    Dokumenttyp Artikel
    Anmerkung NAL-light
    ZDB-ID 1478763-5
    ISSN 0167-9473
    ISSN 0167-9473
    DOI 10.1016/j.csda.2020.107128
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  3. Artikel ; Online: Proteomics and cytokine analyses distinguish myalgic encephalomyelitis/chronic fatigue syndrome cases from controls.

    Giloteaux, Ludovic / Li, Jiayin / Hornig, Mady / Lipkin, W Ian / Ruppert, David / Hanson, Maureen R

    Journal of translational medicine

    2023  Band 21, Heft 1, Seite(n) 322

    Abstract: Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, heterogenous disease characterized by unexplained persistent fatigue and other features including cognitive impairment, myalgias, post-exertional malaise, and immune ... ...

    Abstract Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, heterogenous disease characterized by unexplained persistent fatigue and other features including cognitive impairment, myalgias, post-exertional malaise, and immune system dysfunction. Cytokines are present in plasma and encapsulated in extracellular vesicles (EVs), but there have been only a few reports of EV characteristics and cargo in ME/CFS. Several small studies have previously described plasma proteins or protein pathways that are associated with ME/CFS.
    Methods: We prepared extracellular vesicles (EVs) from frozen plasma samples from a cohort of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) cases and controls with prior published plasma cytokine and plasma proteomics data. The cytokine content of the plasma-derived extracellular vesicles was determined by a multiplex assay and differences between patients and controls were assessed. We then performed multi-omic statistical analyses that considered not only this new data, but extensive clinical data describing the health of the subjects.
    Results: ME/CFS cases exhibited greater size and concentration of EVs in plasma. Assays of cytokine content in EVs revealed IL2 was significantly higher in cases. We observed numerous correlations among EV cytokines, among plasma cytokines, and among plasma proteins from mass spectrometry proteomics. Significant correlations between clinical data and protein levels suggest roles of particular proteins and pathways in the disease. For example, higher levels of the pro-inflammatory cytokines Granulocyte-Monocyte Colony-Stimulating Factor (CSF2) and Tumor Necrosis Factor (TNFα) were correlated with greater physical and fatigue symptoms in ME/CFS cases. Higher serine protease SERPINA5, which is involved in hemostasis, was correlated with higher SF-36 general health scores in ME/CFS. Machine learning classifiers were able to identify a list of 20 proteins that could discriminate between cases and controls, with XGBoost providing the best classification with 86.1% accuracy and a cross-validated AUROC value of 0.947. Random Forest distinguished cases from controls with 79.1% accuracy and an AUROC value of 0.891 using only 7 proteins.
    Conclusions: These findings add to the substantial number of objective differences in biomolecules that have been identified in individuals with ME/CFS. The observed correlations of proteins important in immune responses and hemostasis with clinical data further implicates a disturbance of these functions in ME/CFS.
    Mesh-Begriff(e) Humans ; Cytokines ; Fatigue Syndrome, Chronic ; Proteomics ; Cell Communication ; Case-Control Studies
    Chemische Substanzen Cytokines
    Sprache Englisch
    Erscheinungsdatum 2023-05-13
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 2118570-0
    ISSN 1479-5876 ; 1479-5876
    ISSN (online) 1479-5876
    ISSN 1479-5876
    DOI 10.1186/s12967-023-04179-3
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Artikel ; Online: A semiparametric risk score for physical activity.

    Cui, Erjia / Thompson, E Christi / Carroll, Raymond J / Ruppert, David

    Statistics in medicine

    2021  Band 41, Heft 7, Seite(n) 1191–1204

    Abstract: We develop a generalized partially additive model to build a single semiparametric risk scoring system for physical activity across multiple populations. A score comprised of distinct and objective physical activity measures is a new concept that offers ... ...

    Abstract We develop a generalized partially additive model to build a single semiparametric risk scoring system for physical activity across multiple populations. A score comprised of distinct and objective physical activity measures is a new concept that offers challenges due to the nonlinear relationship between physical behaviors and various health outcomes. We overcome these challenges by modeling each score component as a smooth term, an extension of generalized partially linear single-index models. We use penalized splines and propose two inferential methods, one using profile likelihood and a nonparametric bootstrap, the other using a full Bayesian model, to solve additional computational problems. Both methods exhibit similar and accurate performance in simulations. These models are applied to the National Health and Nutrition Examination Survey and quantify nonlinear and interpretable shapes of score components for all-cause mortality.
    Mesh-Begriff(e) Bayes Theorem ; Exercise ; Humans ; Linear Models ; Models, Statistical ; Nutrition Surveys ; Risk Factors
    Sprache Englisch
    Erscheinungsdatum 2021-11-21
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.9262
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Artikel: Linear Non-Gaussian Component Analysis Via Maximum Likelihood

    Risk, Benjamin B / Matteson, David S / Ruppert, David

    Journal of the American Statistical Association. 2019 Jan. 2, v. 114, no. 525

    2019  

    Abstract: Independent component analysis (ICA) is popular in many applications, including cognitive neuroscience and signal processing. Due to computational constraints, principal component analysis (PCA) is used for dimension reduction prior to ICA (PCA+ICA), ... ...

    Abstract Independent component analysis (ICA) is popular in many applications, including cognitive neuroscience and signal processing. Due to computational constraints, principal component analysis (PCA) is used for dimension reduction prior to ICA (PCA+ICA), which could remove important information. The problem is that interesting independent components (ICs) could be mixed in several principal components that are discarded and then these ICs cannot be recovered. We formulate a linear non-Gaussian component model with Gaussian noise components. To estimate the model parameters, we propose likelihood component analysis (LCA), in which dimension reduction and latent variable estimation are achieved simultaneously. Our method orders components by their marginal likelihood rather than ordering components by variance as in PCA. We present a parametric LCA using the logistic density and a semiparametric LCA using tilted Gaussians with cubic B-splines. Our algorithm is scalable to datasets common in applications (e.g., hundreds of thousands of observations across hundreds of variables with dozens of latent components). In simulations, latent components are recovered that are discarded by PCA+ICA methods. We apply our method to multivariate data and demonstrate that LCA is a useful data visualization and dimension reduction tool that reveals features not apparent from PCA or PCA+ICA. We also apply our method to a functional magnetic resonance imaging experiment from the Human Connectome Project and identify artifacts missed by PCA+ICA. We present theoretical results on identifiability of the linear non-Gaussian component model and consistency of LCA. Supplementary materials for this article are available online.
    Schlagwörter algorithms ; cognition ; equations ; independent component analysis ; magnetic resonance imaging ; models ; neurophysiology ; principal component analysis ; variance
    Sprache Englisch
    Erscheinungsverlauf 2019-0102
    Umfang p. 332-343.
    Erscheinungsort Taylor & Francis
    Dokumenttyp Artikel
    ZDB-ID 2064981-2
    ISSN 1537-274X ; 0003-1291 ; 0162-1459
    ISSN (online) 1537-274X
    ISSN 0003-1291 ; 0162-1459
    DOI 10.1080/01621459.2017.1407772
    Datenquelle NAL Katalog (AGRICOLA)

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  6. Artikel: Dynamic shrinkage processes

    Kowal, Daniel R / Matteson, David S / Ruppert, David

    Journal of the Royal Statistical Society. 2019 Sept., v. 81, no. 4

    2019  

    Abstract: We propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building on a global–local framework of prior construction, in which continuous scale mixtures of Gaussian distributions are employed for both ... ...

    Abstract We propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building on a global–local framework of prior construction, in which continuous scale mixtures of Gaussian distributions are employed for both desirable shrinkage properties and computational tractability, we model dependence between the local scale parameters. The resulting processes inherit the desirable shrinkage behaviour of popular global–local priors, such as the horseshoe prior, but provide additional localized adaptivity, which is important for modelling time series data or regression functions with local features. We construct a computationally efficient Gibbs sampling algorithm based on a Pólya–gamma scale mixture representation of the process proposed. Using dynamic shrinkage processes, we develop a Bayesian trend filtering model that produces more accurate estimates and tighter posterior credible intervals than do competing methods, and we apply the model for irregular curve fitting of minute‐by‐minute Twitter central processor unit usage data. In addition, we develop an adaptive time varying parameter regression model to assess the efficacy of the Fama–French five‐factor asset pricing model with momentum added as a sixth factor. Our dynamic analysis of manufacturing and healthcare industry data shows that, with the exception of the market risk, no other risk factors are significant except for brief periods.
    Schlagwörter Bayesian theory ; algorithms ; assets ; equations ; health services ; manufacturing ; markets ; models ; normal distribution ; prices ; regression analysis ; risk factors ; time series analysis
    Sprache Englisch
    Erscheinungsverlauf 2019-09
    Umfang p. 781-804.
    Erscheinungsort John Wiley & Sons, Ltd
    Dokumenttyp Artikel
    Anmerkung JOURNAL ARTICLE
    ZDB-ID 1490719-7
    ISSN 1467-9868 ; 0035-9246 ; 1369-7412
    ISSN (online) 1467-9868
    ISSN 0035-9246 ; 1369-7412
    DOI 10.1111/rssb.12325
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  7. Artikel ; Online: Benefits of additive manufacturing and micro and nano surface texture modifications on mechanical strength and infection resistance of skin-implant interfaces in rats.

    Lindsay, Christopher / Ruppert, David / Abumoussa, Sam / Dahners, Laurence / Weinhold, Paul

    Journal of biomaterials applications

    2020  Band 34, Heft 9, Seite(n) 1193–1200

    Sprache Englisch
    Erscheinungsdatum 2020-02-10
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 639283-0
    ISSN 1530-8022 ; 0885-3282
    ISSN (online) 1530-8022
    ISSN 0885-3282
    DOI 10.1177/0885328220903961
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Verfügbar in ZB MED Köln/Königswinter

    Zs.B 3175: Hefte anzeigen Standort:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
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  8. Buch ; Online: Statistics and Data Analysis for Financial Engineering

    Ruppert, David

    (Springer Texts in Statistics)

    2011  

    Verfasserangabe by David Ruppert
    Serientitel Springer Texts in Statistics
    Schlagwörter Economics/Statistics ; Statistics
    Sprache Englisch
    Umfang Online-Ressource, v.: digital
    Verlag Springer Science+Business Media, LLC
    Erscheinungsort New York, NY
    Dokumenttyp Buch ; Online
    Anmerkung Description based upon print version of record
    ISBN 9781441977861 ; 9781441977878 ; 1441977864 ; 1441977872
    DOI 10.1007/978-1-4419-7787-8
    Datenquelle Katalog der Technische Informationsbibliothek Hannover

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  9. Buch: Statistics and data analysis for financial engineering

    Ruppert, David

    (Springer texts in statistics)

    2011  

    Verfasserangabe David Ruppert
    Serientitel Springer texts in statistics
    Schlagwörter Finance/Statistical methods ; Financial engineering/Statistical methods ; Financial Engineering ; Datenanalyse ; Statistik ; Statistische Methode
    Sprache Englisch
    Umfang XXII, 638 S., graph. Darst., 24 cm
    Verlag Springer
    Erscheinungsort New York, NY u.a.
    Dokumenttyp Buch
    Anmerkung Literaturangaben
    ISBN 9781441977861 ; 9781441977878 ; 1441977864 ; 1441977872
    Datenquelle ECONomics Information System

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  10. Buch ; Online: Statistics and Data Analysis for Financial Engineering

    Ruppert, David / Matteson, David S

    with R examples

    (Springer Texts in Statistics)

    2015  

    Abstract: The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs ... ...

    Verfasserangabe by David Ruppert, David S. Matteson
    Serientitel Springer Texts in Statistics
    Abstract The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. Financial engineers now have access to enormous quantities of data. To make use of these data, the powerful methods in this book, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, multivariate volatility and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest. David Ruppert is Andrew Schultz, Jr., Professor of Engineering and Professor of Statistical Science at Cornell University, where he teaches statistics and financial engineering and is a member of the Program in Financial Engineering. Professor Ruppert received his PhD in Statistics at Michigan State University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and won the Wilcoxon prize. He is Editor of the Journal of the American Statistical Association-Theory and Methods and former Editor of the Electronic Journal of Statistics and of the Institute of Mathematical Statistics's Lecture Notes-Monographs. Professor Ruppert has published over 125 scientific papers and four books: Transformation and Weighting in Regression, Measurement Error in Nonlinear Models, Semiparametric Regression, and Statistics and Finance: An Introduction. David S. Matteson is Assistant Professor of Statistical Science at Cornell University, where he is a member of the ILR School, Center for Applied Mathematics, Field of Operations Research, and the Program in Financial Engineering, and teaches statistics and financial engineering. Professor Matteson received his PhD in Statistics at the University of Chicago. He received a CAREER Award from the National Science Foundation and won Best Academic Paper Awards from the annual R/Finance conference. He is an Associate Editor of the ...
    Schlagwörter Economics/Statistics ; Finance ; Mathematical statistics ; Statistics
    Sprache Englisch
    Umfang Online-Ressource (XXVI, 719 p. 221 illus., 108 illus. in color), online resource
    Ausgabenhinweis 2nd ed. 2015
    Verlag Springer New York
    Erscheinungsort New York, NY ;s.l
    Dokumenttyp Buch ; Online
    ISBN 9781493926138 ; 9781493926145 ; 1493926136 ; 1493926144
    DOI 10.1007/978-1-4939-2614-5
    Datenquelle Katalog der Technische Informationsbibliothek Hannover

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