Bühlmann, P. (1994). Blockwise bootstrapped empirical processes for
stationary sequences. Annals of Statistics 22, 995-1012.
Bühlmann, P. (1995). The blockwise bootstrap for general empirical
processes of stationary sequences. Stochastic Processes and their
Applications 58, 247-265.
Bühlmann, P. and Künsch, H.R. (1995). The blockwise bootstrap for general
parameters of a stationary time series. Scandinavian Journal of Statistics
22, 35-54.
Bühlmann, P. (1995). Moving-average representation for
autoregressive approximations. Stochastic Processes and
their Applications 60, 331-342.
Bühlmann, P. (1996). Locally adaptive lag-window spectral
estimation. Journal Time Series Analysis 17, 247-270.
Bickel, P.J. and Bühlmann, P. (1996). What is a linear process? Proceedings
National Academy of Sciencies USA 93, 12128-12131.
Bühlmann, P. (1997). Sieve Bootstrap for Time Series. Bernoulli 3, 123-148.
Bickel, P.J. and Bühlmann, P. (1997). Closure of linear
processes. Journal of Theoretical Probability 10, 445-479.
Bühlmann, P. (1998). Extreme events from return-volume process: a
discretization approach for complexity reduction. Applied
Financial Economics 8, 267-278.
Bühlmann, P. (1998). Sieve bootstrap for smoothing in nonstationary time
series. Annals of Statistics 26, 48-83.
Bühlmann, P. (1999). Efficient and adaptive post-model-selection
estimators. Journal of Statistical Planning and Inference 79, 1-9.
Bühlmann, P. and Bühlmann, H. (1999). Selection of credibility regression
models. ASTIN Bulletin (Journal of the International Actuarial
Association) 29, 245-270.
Bühlmann, P. and Künsch, H.R. (1999). Invited Comment on "Prediction of Spatial
Cumulative Distribution Functions Using Subsampling (Lahiri, Kaiser,
Cressie and Hsu)". Journal of the American Statistical Association 94,
97-99.
Bühlmann, P. and Künsch, H.R. (1999). Block length selection in the
bootstrap for time series. Computational Statistics and
Data Analysis 31, 295-310.
Bühlmann, P. and Wyner, A.J. (1999). Variable length Markov chains. Annals
of Statistics 27, 480-513.
Bickel, P.J. and Bühlmann, P. (1999). A new Mixing Notion and
Functional Central Limit Theorems for a Sieve Bootstrap in Time
Series. Bernoulli 5, 413-446.
Bühlmann, P. (2000). Von Daten zu stochastischen Modellen (in
German). Elemente der Mathematik 55, 1-18. Compressed postscript.
Bühlmann, P. (2000). Model selection for variable length Markov chains and
tuning the context algorithm. Annals of the Institute of
Statistical Mathematics 52, 287-315. Compressed postscript.
Bühlmann, P. and Yu, B. (2000). Invited Discussion on "Additive logistic
regression: a statistical view of boosting (Friedman, Hastie and
Tibshirani)". Annals of Statistics 28, 377-386. Compressed postscript. For original paper (Annals of Statistics 28, 337-407) click here.
Audrino, F. and Bühlmann, P. (2001). Tree-structured generalized
autoregressive conditional heteroscedastic models. Journal of the
Royal Statistical Society: Series B, 63, 727-744.
Compressed postscript.
Bühlmann, P. (2002). Sieve bootstrap with variable length Markov chains for
stationary categorical time series (with discussion). Journal
of the American Statistical Association 97, 443-456.
Compressed postscript.
Bühlmann, P. (2002). Rejoinder of "Sieve bootstrap with variable length
Markov chains for stationary categorical time series". Journal of the
American Statistical Association 97, 466-471.
Bühlmann, P. (2002). Bootstraps for time series. Statistical
Science 17, 52-72.
Compressed postscript.
Ango Nze, P., Bühlmann, P. and Doukhan, P. (2002). Weak dependence beyond
mixing and asymptotics for nonparametric regression. Annals of
Statistics 30, 397-430.
Compressed postscript.
Bühlmann, P. and Yu, B. (2002). Analyzing bagging. Annals of Statistics 30,
927-961.
Compressed postscript.
Bühlmann, P. and McNeil, A.J. (2002). An algorithm for nonparametric GARCH
modelling. Computational Statistics and Data Analysis 40, 665-683.
Compressed postscript.
Audrino, F. and Bühlmann, P. (2003). Volatility estimation with functional
gradient descent for very high-dimensional financial time
series. Journal of Computational Finance Vol. 6, No. 3, 65-89.
PDF
Dettling, M. and Bühlmann, P. (2003). Boosting for tumor
classification with gene expression data. Bioinformatics 19, No. 9,
1061-1069.
Compressed postscript.
PDF.Software.
Bühlmann, P. and Yu, B. (2003). Boosting with the L2 loss: regression
and classification. Journal of the American Statistical
Association 98, 324-339.
PDF
Audrino, F. and Bühlmann, P. (2004). Synchronizing multivariate financial
time series. The Journal of Risk 6 (2), 81-106.
PDF
Bühlmann, P. and Yu, B. (2004). Invited Discussion on three papers on
boosting by Jiang, Lugosi and Vayatis, and Zhang. Annals of Statistics 32,
96-101.
PDF
Mächler, M. and Bühlmann, P. (2004). Variable length Markov chains:
methodology, computing and software. Journal of
Computational and Graphical Statistics 13, 435-455. Click here.
Dettling, M. and Bühlmann, P. (2004). Finding predictive gene groups
from microarray data. Journal of Multivariate Analysis 90, 106-131. Compressed
postscript.
PDF
Dettling, M. and Bühlmann, P. (2004). Volatility and risk estimation with
linear and nonlinear methods based on high frequency data. Applied
Financial Economics 14, 717-729. PDF.
Teuffel, O., Dettling, M., Cario, G., Stanulla, M., Schrappe, M., Bühlmann,
P., Niggli, F. and Schäfer, B. (2004). Gene expression profiles and risk
stratification in childhood acute lymphoblastic leukemia. Haematologica 89,
801-808.
Wachtel, M., Dettling, M., Koscielniak, E., Stegmaier, S., Treuner,
J., Simon-Klingenstein, K., Bühlmann, P., Niggli, F. and Schäfer,B. (2004).
Gene expression signatures identify rhabdomyosarcoma subtypes and detect a
novel t(2;2)(q35;p23) translocation fusing PAX3 to NCOA1. Cancer Research
64, 5539-5545.
Wille, A., Zimmermann, P., Vranova, E., Fürholz, A., Laule, O., Bleuler,
S., Hennig, L., Prelic, A., von Rohr, P., Thiele, L., Zitzler, E.,
Gruissem, W. and Bühlmann, P. (2004). Sparse graphical Gaussian modeling
of the isoprenoid gene network in Arabidopsis thaliana.Genome Biology
5(11) R92, 1-13.
Meinshausen, N. and Bühlmann, P. (2005). Lower bounds for the number of
false null hypotheses for multiple testing of associations under general
dependence structures. Biometrika 92, 893-907.
PDF
Wille, A. and Bühlmann, P. (2006). Low-order conditional independence
graphs for inferring genetic networks. Statistical
Applications in Genetics and Molecular Biology 5 (1) Art1, 1-32.Download paper.
Prelic, A., Bleuler, S., Zimmermann, P., Wille, A., Bühlmann, P.,
Gruissem, W., Hennig, L., Thiele, L. and Zitzler, E. (2006). A systematic
comparison and evaluation of biclustering methods for gene expression
data. Bioinformatics 22, 1122-1129. Download paper.
BicAT: A Biclustering Analysis Toolbox.
Bühlmann, P. (2006). Boosting for high-dimensional linear models. Annals of
Statistics 34, 559-583.
PDF
Lutz, R.W. and Bühlmann, P. (2006). Boosting for high-multivariate
responses in high-dimensional linear regression. Statistica Sinica 16,
471-494.
PDF
Lutz, R.W. and Bühlmann, P. (2006). Conjugate direction boosting. Journal
of Computational and Graphical Statistics 15, 287-311.
PDF
Bühlmann, P. and Yu, B. (2006). Sparse Boosting. Journal of Machine
Learning Research 7, 1001-1024.
PDF
Hothorn, T., Bühlmann, P., Dudoit, S., Molinaro, A. and van der Laan,
M. (2006). Survival ensembles. Biostatistics 7, 355-373.Download paper.
Meinshausen, N. and Bühlmann, P. (2006). High-dimensional graphs and
variable selection with the Lasso. Annals of Statistics 34, 1436-1462.
PDF. According to Essential Science Indicators, this has been selected
as New Hot Paper.
Hothorn, T. and Bühlmann, P. (2006). Model-based boosting in high
dimensions. Bioinformatics 22, 2828-2829. PDF
Goeman, J.J. and Bühlmann, P. (2007). Analyzing gene expression data
in terms of gene sets: methodological issues. Bioinformatics 23, 980-987. PDF
Kalisch, M. and Bühlmann, P. (2007). Estimating high-dimensional
directed acyclic graphs with the PC-algorithm. Journal of Machine
Learning Research 8, 613-636.
PDF
Elsener, A., Samson, C.C.M., Brändle, M.P., Bühlmann, P. and Lüthi,
H.P. (2007). Statistical analysis of quantum chemical data using
generalized XML/CML archives for the derivation of molecular design
rules. Chimia 61, 165-168. PDF
Wille, A., Gruissem, W., Bühlmann, P. and Hennig, L. (2007). EVE
(External Variance Estimation) increases statistical power for detecting
differentially expressed genes. The Plant Journal 52, 561-569.PDF
Bühlmann, P. (2007). Bootstrap schemes for time series (in
Russian). Quantile 3, 37-56.PDF
Meier, L. and Bühlmann, P. (2007). Smoothing L1-penalized estimators
for high-dimensional time-course data. Electronic Journal of
Statistics 1, 597-615.
PDF
Bühlmann, P. and Hothorn, T. (2007). Boosting algorithms:
regularization, prediction and model fitting (with
discussion). Statistical Science
22, 477-505. (The paper includes supporting software). PDF
Bühlmann, P. and Hothorn, T. (2007). Rejoinder of "Boosting algorithms:
regularization, prediction and model
fitting". Statistical Science
22, 516-522. PDF
Dahinden, C., Parmigiani, G., Emerick, M.C. and Bühlmann,
P. (2007). Penalized likelihood for sparse contingency tables with an
application to full-length cDNA libraries. BMC Bioinformatics 2007,
8:476, 1-11.
Meier, L., van de Geer, S. and Bühlmann, P. (2008). The Group Lasso
for logistic regression. Journal of the Royal Statistical Society: Series
B, 70, 53-71.
PDF
Schöner, D., Kalisch, M., Leisner, C., Meier, L., Sohrmann, M., Faty,
M., Barral, Y., Peter, M., Gruissem, W. and Bühlmann,
P. (2008). Annotating novel genes by integrating synthetic lethals and
genomic information. BMC Systems Biology 2008,
2:3, 1-14.
Bühlmann, P. and Yu, B. (2008). Invited Discussion on "Evidence
contrary to the statistical view of boosting (D. Mease and A. Wyner)".
Journal of Machine Learning Research 9,
187-194. Download paper with discussion.
Lutz, R.W., Kalisch, M. and Bühlmann, P. (2008). Robustified
L2 boosting. Computational Statistics & Data Analysis 52, 3331-3341.
PDF
Meinshausen, N. and Bühlmann, P. (2008). Invited Discussion on "Treelets -
An adaptive multi-scale basis for sparse unordered data (A.B. Lee,
B. Nadler and L. Wasserman)". Annals of Applied
Statistics 2, 478-481. PDF
Bühlmann, P. and Meier, L. (2008). Invited Discussion on "One-step
sparse estimates in nonconcave penalized likelihood models (H. Zou and
R. Li)". Annals of Statistics 36,
1534-1541. PDF
Lange, V., Malmström, J. A., Didion, J., King, N. L., Johansson,
B. P., Schäfer, J., Rameseder, J., Wong, C.-H., Deutsch, E. W., Brusniak,
M.-Y., Bühlmann, P., Björck, L., Domon, B. and Aebersold,
R. (2008). Targeted quantitative analysis of Streptococcus pyogenes
virulence factors by multiple reaction monitoring. Molecular
& Cellular Proteomics 7, 1489-1500.
Download paper.
Bühlmann, P. (2008). Invited Discussion on "Sure Independence Screening
(J. Fan and J. Lv)". Journal of the Royal Statistical Society: Series B,
70, 884-887.
PDF
Kalisch, M. and Bühlmann, P. (2008). Robustification of the
PC-algorithm for directed acyclic graphs. Journal of Computational and
Graphical Statistics 17, 773-789.
PDF
Audrino, F. and Bühlmann, P. (2009). Splines for financial
volatility. Journal of the Royal Statistical Society: Series B, 71,
655-670.
PDF
Maathuis, M.H., Kalisch, M. and Bühlmann, P. (2009). Estimating
high-dimensional intervention effects from observational data. Annals of
Statistics 37, 3133-3164.PDF
Meier, L., van de Geer, S. and Bühlmann, P. (2009). High-dimensional
additive modeling. Annals of Statistics 37,
3779-3821. PDF
Buller, F., Zhang, Y., Scheuermann, J., Schäfer, J., Bühlmann, P. and
Neri, D. (2009). Discovery of TNF inhibitors from a DNA-encoded chemical
library based on Diels-Alder cycloaddition. Chemistry & Biology 16,
1075-1086.
Rütimann, P. and Bühlmann, P. (2009). High dimensional sparse covariance
estimation via directed acyclic graphs. Electronic Journal of Statistics 3,
1133-1160. PDF
van de Geer, S. and Bühlmann, P. (2009). On the conditions used to
prove oracle results for the Lasso. Electronic Journal
of Statistics 3,
1360-1392. PDF
Dahinden, C., Ingold, B., Wild, P., Boysen, G., Luu, V.-D., Montani,
M., Kristiansen, G., Sulser, T., Bühlmann, P., Moch, H., Schraml,
P. (2010). Mining tissue microarray data to uncover combinations of
biomarker expression patterns that improve intermediate staging and
grading of clear cell renal cell cancer. Clinical Cancer Research 16,
88-98.
Bühlmann, P. and Hothorn, T. (2009). Twin Boosting: improved feature
selection and prediction. To appear in Statistics and Computing. PDF
Meinshausen, N. and Bühlmann, P. (2008). Stability
selection. To appear as discussion paper in the Journal of the Royal
Statistical Society, Series B. arXiv:0809.2932v2
Meinshausen, N., Meier, L. and Bühlmann, P. (2009). P-values for
high-dimensional regression. To appear in the Journal of the American
Statistical Association. arXiv:0811.2177v3
Bühlmann, P., Kalisch, M. and Maathuis, M.H. (2009). Variable selection for
high-dimensional linear models: partially faithful distributions and the
PC-simple algorithm. To appear in
Biometrika. arXiv:0906.3204v3
Dahinden, C., Kalisch, M. and Bühlmann, P. (2009). Decomposition and model
selection for large contingency tables. To appear in Biometrical
Journal. arXiv:0904.1510v2
Book chapters
Bühlmann, P. (2001). Time series. Encyclopedia of Environmetrics
(eds. El-Shaarawi, A.H. and Piegorsch, W.W.) , Vol. 4, pp. 2187--2202.
Bühlmann, P. (2003). Bagging, subagging and bragging for improving some
prediction algorithms. In Recent Advances and Trends in
Nonparametric Statistics (eds. Akritas, M.G. and Politis, D.N.),
pp. 19-34. Elsevier. PDF
Bühlmann, P. (2004). Bagging, boosting and ensemble methods. In Handbook of
Computational Statistics: Concepts and Methods (eds. Gentle, J., Härdle,
W. and Mori, Y.), pp. 877-907. Springer.
Hothorn, T., Dettling, M. and Bühlmann, P. (2005). Computational
inference. In Bioinformatics and Computational Biology Solutions using R and
Bioconductor (eds. Gentleman, R., Carey, V., Huber, W., Irizarry, R. and
Dudoit, S.), pp. 293-312. Springer. PDF. See also
here.
Bühlmann, P. (2006). Boosting and l^1-penalty methods for
high-dimensional data with some applications in genomics. In From Data and
Information Analysis to Knowledge Engineering (eds. Spiliopoulou, M.,
Kruse, R., Borgelt, C., Nürnberger, A. and Gaul, W.), pp. 1-12. Studies in
Classification, Data Analysis, and Knowledge Organization, Springer.
Bühlmann, P. and Lutz, R.W. (2006). Boosting algorithms: with an
application to bootstrapping multivariate time series. In Frontiers in
Statistics (eds. Fan, J. and Koul, H.), pp. 209-230. Imperial College Press.
PDF
Schöner D., Barkow S., Bleuler S., Wille A., Zimmermmann P., Bühlmann P.,
Gruissem W. and Zitzler, E. (2007). Network Analysis of Systems
Elements. In Plant Systems Biology, Series: Experientia Supplementum
(eds. Baginsky, S. and Fernie A), pp. 331-351. Birkhäuser.
Audrino, F. and Bühlmann, P. (2007). Synchronizing multivariate financial
time series. In The Value-at-Risk Reference (ed. Danielsson,
J.), pp. 261-291. Riskbooks.
Bühlmann, P. and Yu, B. (2009). Boosting. To appear in Wiley
Interdisciplinary Reviews: Computational Statistics. PDF
Proceedings
Bühlmann, P. (1999). Bootstrapping time series. Bulletin
of the International Statistical Institute, 52nd session. Proceedings, Tome
LVIII, Book1, 201-204.
Bühlmann, P. (2003). Boosting methods: why they can be useful for
high-dimensional data. Proceedings of the 3rd International Workshop on
Distributed Computing (DSC 2003).
PDF
Bühlmann, P. (2007). Variable selection for high-dimensional data: with
applications in molecular biology. Bulletin
of the International Statistical Institute, 56nd session. PDF
Schäfer, J. and Bühlmann, P. (2007). Modeling inhomogeneous
high-dimensional data-sets: with applications in learning large-scale gene
correlations. S.Co. 2007. PDF
Preprints
Zhou, S., van de Geer, S. and Bühlmann, P. (2009). Adaptive Lasso for high
dimensional regression and Gaussian graphical
modeling. Preprint arXiv:0903.2515v1
Städler, N. and Bühlmann, P. (2009). Missing values and sparse inverse
covariance estimation. Preprint arXiv:0903.5463v1
Städler, N., Bühlmann, P. and van de Geer, S. (2009). l1-penalization
for mixture regression
models. PDF
Schöner, D., Dahinden, C., Gruissem, W. and Bühlmann,
P. (2009). Robust prediction of hubs in the yeast synthetic lethal
network.
Versions of Published and Unpublished Papers
Bühlmann, P. (1996). Confidence regions for trends in time series:
a Simultaneous Approach with a Sieve Bootstrap. Tech. Rep. 447. UC
Berkeley. Succeeded by Bühlmann (1998): Sieve bootstrap for smoothing in
nonstationary time series (see above No. 10).
Bühlmann, P. (2002). Consistency for L2Boosting and matching pursuit with
trees and tree-type basis functions. Succeeded by Bühlmann (2004):
Boosting for high-dimensional linear models (see above No. 42).
Bühlmann, P. and Ferrari, F. (2003). Dynamic combination of models for
nonlinear time series.
PDF
Wille, A. and Bühlmann, P. (2004). Tri-Graph: a novel graphical model with
application to genetic regulatory networks. Succeeded by Wille and
Bühlmann (2006): Low-order conditional independence
graphs for inferring genetic networks (see above No. 40).
Bühlmann, P. and Yu, B. (2005). Boosting, model selection, lasso and
nonnegative garrote. Succeeded by Bühlmann and Yu (2005): Sparse Boosting
(see above No. 45).
Wille, A., Bleuler, S. and Bühlmann, P. (2005). Integrating gene expression
data into flux balance analysis.