Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1 to 12 of 18 entries
Sorted by: Best Match Show Resources per page
Asymptotics of nonparametric L-1 regression models with dependent data.

Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability

Zhao Z, Wei Y, Lin DK.
PMID: 24955016
Bernoulli (Andover). 2014 Aug 01;20(3):1532-1559. doi: 10.3150/13-BEJ532.

We investigate asymptotic properties of least-absolute-deviation or median quantile estimates of the location and scale functions in nonparametric regression models with dependent data from multiple subjects. Under a general dependence structure that allows for longitudinal data and some spatially...

Cite
Zhao Z, Wei Y, Lin DK. Asymptotics of nonparametric L-1 regression models with dependent data. Bernoulli (Andover). 2014;20(3):1532-1559doi: 10.3150/13-BEJ532.
Zhao, Z., Wei, Y., & Lin, D. K. (2014). Asymptotics of nonparametric L-1 regression models with dependent data. Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, 20(3), 1532-1559. https://doi.org/10.3150/13-BEJ532
Zhao, Zhibiao, et al. "Asymptotics of nonparametric L-1 regression models with dependent data." Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability vol. 20,3 (2014): 1532-1559. doi: https://doi.org/10.3150/13-BEJ532
Zhao Z, Wei Y, Lin DK. Asymptotics of nonparametric L-1 regression models with dependent data. Bernoulli (Andover). 2014 Aug 01;20(3):1532-1559. doi: 10.3150/13-BEJ532. PMID: 24955016; PMCID: PMC4060752.
Copy Download .nbib Format: NLM
Simultaneous Critical Values For T-Tests In Very High Dimensions.

Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability

Cao H, Kosorok MR.
PMID: 21572564
Bernoulli (Andover). 2011 Feb;17(1):347-394. doi: 10.3150/10-BEJ272.

This article considers the problem of multiple hypothesis testing using t-tests. The observed data are assumed to be independently generated conditional on an underlying and unknown two-state hidden model. We propose an asymptotically valid data-driven procedure to find critical...

Cite
Cao H, Kosorok MR. Simultaneous Critical Values For T-Tests In Very High Dimensions. Bernoulli (Andover). 2011;17(1):347-394doi: 10.3150/10-BEJ272.
Cao, H., & Kosorok, M. R. (2011). Simultaneous Critical Values For T-Tests In Very High Dimensions. Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, 17(1), 347-394. https://doi.org/10.3150/10-BEJ272
Cao, Hongyuan, and Kosorok, Michael R. "Simultaneous Critical Values For T-Tests In Very High Dimensions." Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability vol. 17,1 (2011): 347-394. doi: https://doi.org/10.3150/10-BEJ272
Cao H, Kosorok MR. Simultaneous Critical Values For T-Tests In Very High Dimensions. Bernoulli (Andover). 2011 Feb;17(1):347-394. doi: 10.3150/10-BEJ272. PMID: 21572564; PMCID: PMC3092179.
Copy Download .nbib Format: NLM
Information bounds for Gaussian copulas.

Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability

Hoff PD, Niu X, Wellner JA.
PMID: 25313292
Bernoulli (Andover). 2014;20(2):604-622. doi: 10.3150/12-BEJ499.

Often of primary interest in the analysis of multivariate data are the copula parameters describing the dependence among the variables, rather than the univariate marginal distributions. Since the ranks of a multivariate dataset are invariant to changes in the...

Cite
Hoff PD, Niu X, Wellner JA. Information bounds for Gaussian copulas. Bernoulli (Andover). 2014;20(2):604-622doi: 10.3150/12-BEJ499.
Hoff, P. D., Niu, X., & Wellner, J. A. (2014). Information bounds for Gaussian copulas. Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, 20(2), 604-622. https://doi.org/10.3150/12-BEJ499
Hoff, Peter D, et al. "Information bounds for Gaussian copulas." Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability vol. 20,2 (2014): 604-622. doi: https://doi.org/10.3150/12-BEJ499
Hoff PD, Niu X, Wellner JA. Information bounds for Gaussian copulas. Bernoulli (Andover). 2014;20(2):604-622. doi: 10.3150/12-BEJ499. PMID: 25313292; PMCID: PMC4193671.
Copy Download .nbib Format: NLM
Universal sieve-based strategies for efficient estimation using machine learning tools.

Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability

Qiu H, Luedtke A, Carone M.
PMID: 34733110
Bernoulli (Andover). 2021 Nov;27(4):2300-2336. doi: 10.3150/20-BEJ1309. Epub 2021 Aug 24.

Suppose that we wish to estimate a finite-dimensional summary of one or more function-valued features of an underlying data-generating mechanism under a nonparametric model. One approach to estimation is by plugging in flexible estimates of these features. Unfortunately, in...

Cite
Qiu H, Luedtke A, Carone M. Universal sieve-based strategies for efficient estimation using machine learning tools. Bernoulli (Andover). 2021;27(4):2300-2336doi: 10.3150/20-BEJ1309.
Qiu, H., Luedtke, A., & Carone, M. (2021). Universal sieve-based strategies for efficient estimation using machine learning tools. Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, 27(4), 2300-2336. https://doi.org/10.3150/20-BEJ1309
Qiu, Hongxiang, et al. "Universal sieve-based strategies for efficient estimation using machine learning tools." Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability vol. 27,4 (2021): 2300-2336. doi: https://doi.org/10.3150/20-BEJ1309
Qiu H, Luedtke A, Carone M. Universal sieve-based strategies for efficient estimation using machine learning tools. Bernoulli (Andover). 2021 Nov;27(4):2300-2336. doi: 10.3150/20-BEJ1309. Epub 2021 Aug 24. PMID: 34733110; PMCID: PMC8561841.
Copy Download .nbib Format: NLM
Stein's method and approximating the quantum harmonic oscillator.

Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability

McKeague IW, Peköz EA, Swan Y.
PMID: 31178654
Bernoulli (Andover). 2019 Feb;25(1):89-111. doi: 10.3150/17-BEJ960. Epub 2018 Dec 12.

Hall et al. (2014) recently proposed that quantum theory can be understood as the continuum limit of a deterministic theory in which there is a large, but finite, number of classical "worlds." A resulting Gaussian limit theorem for particle...

Cite
McKeague IW, Peköz EA, Swan Y. Stein's method and approximating the quantum harmonic oscillator. Bernoulli (Andover). 2018;25(1):89-111doi: 10.3150/17-BEJ960.
McKeague, I. W., Peköz, E. A., & Swan, Y. (2019). Stein's method and approximating the quantum harmonic oscillator. Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, 25(1), 89-111. https://doi.org/10.3150/17-BEJ960
McKeague, Ian W, et al. "Stein's method and approximating the quantum harmonic oscillator." Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability vol. 25,1 (2019): 89-111. doi: https://doi.org/10.3150/17-BEJ960
McKeague IW, Peköz EA, Swan Y. Stein's method and approximating the quantum harmonic oscillator. Bernoulli (Andover). 2019 Feb;25(1):89-111. doi: 10.3150/17-BEJ960. Epub 2018 Dec 12. PMID: 31178654; PMCID: PMC6550468.
Copy Download .nbib Format: NLM
Variable Selection in Measurement Error Models.

Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability

Ma Y, Li R.
PMID: 20209020
Bernoulli (Andover). 2010;16(1):274-300. doi: 10.3150/09-bej205.

Measurement error data or errors-in-variable data are often collected in many studies. Natural criterion functions are often unavailable for general functional measurement error models due to the lack of information on the distribution of the unobservable covariates. Typically, the...

Cite
Ma Y, Li R. Variable Selection in Measurement Error Models. Bernoulli (Andover). 2010;16(1):274-300doi: 10.3150/09-bej205.
Ma, Y., & Li, R. (2010). Variable Selection in Measurement Error Models. Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, 16(1), 274-300. https://doi.org/10.3150/09-bej205
Ma, Yanyuan, and Li, Runze. "Variable Selection in Measurement Error Models." Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability vol. 16,1 (2010): 274-300. doi: https://doi.org/10.3150/09-bej205
Ma Y, Li R. Variable Selection in Measurement Error Models. Bernoulli (Andover). 2010;16(1):274-300. doi: 10.3150/09-bej205. PMID: 20209020; PMCID: PMC2832228.
Copy Download .nbib Format: NLM
Empirical likelihood-based tests for stochastic ordering.

Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability

Barmi HE, McKeague IW.
PMID: 23874142
Bernoulli (Andover). 2013;19(1):295-307. doi: 10.3150/11-BEJ393SUPP.

This paper develops an empirical likelihood approach to testing for the presence of stochastic ordering among univariate distributions based on independent random samples from each distribution. The proposed test statistic is formed by integrating a localized empirical likelihood statistic...

Cite
Barmi HE, McKeague IW. Empirical likelihood-based tests for stochastic ordering. Bernoulli (Andover). 2013;19(1):295-307doi: 10.3150/11-BEJ393SUPP.
Barmi, H. E., & McKeague, I. W. (2013). Empirical likelihood-based tests for stochastic ordering. Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, 19(1), 295-307. https://doi.org/10.3150/11-BEJ393SUPP
Barmi, Hammou El, and McKeague, Ian W. "Empirical likelihood-based tests for stochastic ordering." Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability vol. 19,1 (2013): 295-307. doi: https://doi.org/10.3150/11-BEJ393SUPP
Barmi HE, McKeague IW. Empirical likelihood-based tests for stochastic ordering. Bernoulli (Andover). 2013;19(1):295-307. doi: 10.3150/11-BEJ393SUPP. PMID: 23874142; PMCID: PMC3716296.
Copy Download .nbib Format: NLM
Exponential bounds for the hypergeometric distribution.

Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability

Greene E, Wellner JA.
PMID: 29520197
Bernoulli (Andover). 2017 Aug;23(3):1911-1950. doi: 10.3150/15-BEJ800. Epub 2017 Mar 17.

We establish exponential bounds for the hypergeometric distribution which include a finite sampling correction factor, but are otherwise analogous to bounds for the binomial distribution due to León and Perron (

Cite
Greene E, Wellner JA. Exponential bounds for the hypergeometric distribution. Bernoulli (Andover). 2017;23(3):1911-1950doi: 10.3150/15-BEJ800.
Greene, E., & Wellner, J. A. (2017). Exponential bounds for the hypergeometric distribution. Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, 23(3), 1911-1950. https://doi.org/10.3150/15-BEJ800
Greene, Evan, and Wellner, Jon A. "Exponential bounds for the hypergeometric distribution." Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability vol. 23,3 (2017): 1911-1950. doi: https://doi.org/10.3150/15-BEJ800
Greene E, Wellner JA. Exponential bounds for the hypergeometric distribution. Bernoulli (Andover). 2017 Aug;23(3):1911-1950. doi: 10.3150/15-BEJ800. Epub 2017 Mar 17. PMID: 29520197; PMCID: PMC5839521.
Copy Download .nbib Format: NLM
On the maximal size of large-average and ANOVA-fit submatrices in a Gaussian random matrix.

Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability

Sun X, Nobel AB.
PMID: 24194673
Bernoulli (Andover). 2013;19(1):275-294. doi: 10.3150/11-bej394.

We investigate the maximal size of distinguished submatrices of a Gaussian random matrix. Of interest are submatrices whose entries have an average greater than or equal to a positive constant, and submatrices whose entries are well fit by a...

Cite
Sun X, Nobel AB. On the maximal size of large-average and ANOVA-fit submatrices in a Gaussian random matrix. Bernoulli (Andover). 2013;19(1):275-294doi: 10.3150/11-bej394.
Sun, X., & Nobel, A. B. (2013). On the maximal size of large-average and ANOVA-fit submatrices in a Gaussian random matrix. Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, 19(1), 275-294. https://doi.org/10.3150/11-bej394
Sun, Xing, and Nobel, Andrew B. "On the maximal size of large-average and ANOVA-fit submatrices in a Gaussian random matrix." Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability vol. 19,1 (2013): 275-294. doi: https://doi.org/10.3150/11-bej394
Sun X, Nobel AB. On the maximal size of large-average and ANOVA-fit submatrices in a Gaussian random matrix. Bernoulli (Andover). 2013;19(1):275-294. doi: 10.3150/11-bej394. PMID: 24194673; PMCID: PMC3816128.
Copy Download .nbib Format: NLM
Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution.

Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability

Han F, Liu H.
PMID: 28337068
Bernoulli (Andover). 2017 Feb;23(1):23-57. doi: 10.3150/15-BEJ702. Epub 2016 Sep 27.

Correlation matrix plays a key role in many multivariate methods (e.g., graphical model estimation and factor analysis). The current state-of-the-art in estimating large correlation matrices focuses on the use of Pearson's sample correlation matrix. Although Pearson's sample correlation matrix...

Cite
Han F, Liu H. Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution. Bernoulli (Andover). 2016;23(1):23-57doi: 10.3150/15-BEJ702.
Han, F., & Liu, H. (2017). Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution. Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, 23(1), 23-57. https://doi.org/10.3150/15-BEJ702
Han, Fang, and Liu, Han. "Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution." Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability vol. 23,1 (2017): 23-57. doi: https://doi.org/10.3150/15-BEJ702
Han F, Liu H. Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution. Bernoulli (Andover). 2017 Feb;23(1):23-57. doi: 10.3150/15-BEJ702. Epub 2016 Sep 27. PMID: 28337068; PMCID: PMC5360110.
Copy Download .nbib Format: NLM
Inference for modulated stationary processes.

Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability

Zhao Z, Li X.
PMID: 23539557
Bernoulli (Andover). 2013 Feb 01;19(1):205-227. doi: 10.3150/11-BEJ399.

We study statistical inferences for a class of modulated stationary processes with time-dependent variances. Due to non-stationarity and the large number of unknown parameters, existing methods for stationary or locally stationary time series are not applicable. Based on a...

Cite
Zhao Z, Li X. Inference for modulated stationary processes. Bernoulli (Andover). 2013;19(1):205-227doi: 10.3150/11-BEJ399.
Zhao, Z., & Li, X. (2013). Inference for modulated stationary processes. Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, 19(1), 205-227. https://doi.org/10.3150/11-BEJ399
Zhao, Zhibiao, and Li, Xiaoye. "Inference for modulated stationary processes." Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability vol. 19,1 (2013): 205-227. doi: https://doi.org/10.3150/11-BEJ399
Zhao Z, Li X. Inference for modulated stationary processes. Bernoulli (Andover). 2013 Feb 01;19(1):205-227. doi: 10.3150/11-BEJ399. PMID: 23539557; PMCID: PMC3607552.
Copy Download .nbib Format: NLM
Expected Number and Height Distribution of Critical Points of Smooth Isotropic Gaussian Random Fields.

Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability

Cheng D, Schwartzman A.
PMID: 31511762
Bernoulli (Andover). 2018 Nov;24(4):3422-3446. doi: 10.3150/17-BEJ964. Epub 2018 Apr 18.

We obtain formulae for the expected number and height distribution of critical points of smooth isotropic Gaussian random fields parameterized on Euclidean space or spheres of arbitrary dimension. The results hold in general in the sense that there are...

Cite
Cheng D, Schwartzman A. Expected Number and Height Distribution of Critical Points of Smooth Isotropic Gaussian Random Fields. Bernoulli (Andover). 2018;24(4):3422-3446doi: 10.3150/17-BEJ964.
Cheng, D., & Schwartzman, A. (2018). Expected Number and Height Distribution of Critical Points of Smooth Isotropic Gaussian Random Fields. Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, 24(4), 3422-3446. https://doi.org/10.3150/17-BEJ964
Cheng, Dan, and Schwartzman, Armin. "Expected Number and Height Distribution of Critical Points of Smooth Isotropic Gaussian Random Fields." Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability vol. 24,4 (2018): 3422-3446. doi: https://doi.org/10.3150/17-BEJ964
Cheng D, Schwartzman A. Expected Number and Height Distribution of Critical Points of Smooth Isotropic Gaussian Random Fields. Bernoulli (Andover). 2018 Nov;24(4):3422-3446. doi: 10.3150/17-BEJ964. Epub 2018 Apr 18. PMID: 31511762; PMCID: PMC6738978.
Copy Download .nbib Format: NLM
Showing 1 to 12 of 18 entries