Additive Model - Explore the Science & Experts | ideXlab

Scan Science and Technology

Contact Leading Edge Experts & Companies

Additive Model

The Experts below are selected from a list of 138963 Experts worldwide ranked by ideXlab platform

Additive Model – Free Register to Access Experts & Abstracts

Pei Fen Kuan – One of the best experts on this subject based on the ideXlab platform.

  • negative binomial Additive Model for rna seq data analysis
    BMC Bioinformatics, 2020
    Co-Authors: Xu Ren, Pei Fen Kuan
    Abstract:

    High-throughput sequencing experiments followed by differential expression analysis is a widely used approach for detecting genomic biomarkers. A fundamental step in differential expression analysis is to Model the association between gene counts and covariates of interest. Existing Models assume linear effect of covariates, which is restrictive and may not be sufficient for certain phenotypes. We introduce NBAMSeq, a flexible statistical Model based on the generalized Additive Model and allows for information sharing across genes in variance estimation. Specifically, we Model the logarithm of mean gene counts as sums of smooth functions with the smoothing parameters and coefficients estimated simultaneously within a nested iterative method. The variance is estimated by the Bayesian shrinkage approach to fully exploit the information across all genes. Based on extensive simulations and case studies of RNA-Seq data, we show that NBAMSeq offers improved performance in detecting nonlinear effect and maintains equivalent performance in detecting linear effect compared to existing methods. The vignette and source code of NBAMSeq are available at http://bioconductor.org/packages/release/bioc/html/NBAMSeq.html.

  • negative binomial Additive Model for rna seq data analysis
    bioRxiv, 2019
    Co-Authors: Xu Ren, Pei Fen Kuan
    Abstract:

    SUMMARY High-throughput sequencing experiments followed by differential expression analysis is a widely used approach for detecting genomic biomarkers. A fundamental step in differential expression analysis is to Model the association between gene counts and co-variates of interest. Existing Models assume linear effect of covariates, which is restrictive and may not be sufficient for some phenotypes. In this paper, we introduce NBAMSeq, a flexible statistical Model based on the generalized Additive Model and allows for information sharing across genes in variance estimation. Specifically, we Model the logarithm of mean gene counts as sums of smooth functions with the smoothing parameters and coefficients estimated simultaneously within a nested iterative method. The variance is estimated by the Bayesian shrinkage approach to fully exploit the information across all genes. Based on extensive simulation and case studies of RNA-Seq data, we show that NBAMSeq offers improved performance in detecting nonlinear effect and maintains equivalent performance in detecting linear effect compared to existing methods. Our proposed NBAMSeq is available for download at https://github.com/reese3928/NBAMSeq and in submission to Bioconductor repository.

Han Liu – One of the best experts on this subject based on the ideXlab platform.

  • kernel meets sieve post regularization confidence bands for sparse Additive Model
    Journal of the American Statistical Association, 2020
    Co-Authors: Mladen Kolar, Han Liu
    Abstract:

    We develop a novel procedure for constructing confidence bands for components of a sparse Additive Model. Our procedure is based on a new kernel-sieve hybrid estimator that combines two most popula…

  • Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model.
    arXiv: Machine Learning, 2015
    Co-Authors: Mladen Kolar, Han Liu
    Abstract:

    We develop a novel procedure for constructing confidence bands for components of a sparse Additive Model. Our procedure is based on a new kernel-sieve hybrid estimator that combines two most popular nonparametric estimation methods in the literature, the kernel regression and the spline method, and is of interest in its own right. Existing methods for fitting sparse Additive Model are primarily based on sieve estimators, while the literature on confidence bands for nonparametric Models are primarily based upon kernel or local polynomial estimators. Our kernel-sieve hybrid estimator combines the best of both worlds and allows us to provide a simple procedure for constructing confidence bands in high-dimensional sparse Additive Models. We prove that the confidence bands are asymptotically honest by studying approximation with a Gaussian process. Thorough numerical results on both synthetic data and real-world neuroscience data are provided to demonstrate the efficacy of the theory.

Xu Ren – One of the best experts on this subject based on the ideXlab platform.

  • negative binomial Additive Model for rna seq data analysis
    BMC Bioinformatics, 2020
    Co-Authors: Xu Ren, Pei Fen Kuan
    Abstract:

    High-throughput sequencing experiments followed by differential expression analysis is a widely used approach for detecting genomic biomarkers. A fundamental step in differential expression analysis is to Model the association between gene counts and covariates of interest. Existing Models assume linear effect of covariates, which is restrictive and may not be sufficient for certain phenotypes. We introduce NBAMSeq, a flexible statistical Model based on the generalized Additive Model and allows for information sharing across genes in variance estimation. Specifically, we Model the logarithm of mean gene counts as sums of smooth functions with the smoothing parameters and coefficients estimated simultaneously within a nested iterative method. The variance is estimated by the Bayesian shrinkage approach to fully exploit the information across all genes. Based on extensive simulations and case studies of RNA-Seq data, we show that NBAMSeq offers improved performance in detecting nonlinear effect and maintains equivalent performance in detecting linear effect compared to existing methods. The vignette and source code of NBAMSeq are available at http://bioconductor.org/packages/release/bioc/html/NBAMSeq.html.

  • negative binomial Additive Model for rna seq data analysis
    bioRxiv, 2019
    Co-Authors: Xu Ren, Pei Fen Kuan
    Abstract:

    SUMMARY High-throughput sequencing experiments followed by differential expression analysis is a widely used approach for detecting genomic biomarkers. A fundamental step in differential expression analysis is to Model the association between gene counts and co-variates of interest. Existing Models assume linear effect of covariates, which is restrictive and may not be sufficient for some phenotypes. In this paper, we introduce NBAMSeq, a flexible statistical Model based on the generalized Additive Model and allows for information sharing across genes in variance estimation. Specifically, we Model the logarithm of mean gene counts as sums of smooth functions with the smoothing parameters and coefficients estimated simultaneously within a nested iterative method. The variance is estimated by the Bayesian shrinkage approach to fully exploit the information across all genes. Based on extensive simulation and case studies of RNA-Seq data, we show that NBAMSeq offers improved performance in detecting nonlinear effect and maintains equivalent performance in detecting linear effect compared to existing methods. Our proposed NBAMSeq is available for download at https://github.com/reese3928/NBAMSeq and in submission to Bioconductor repository.

P Vainikainen – One of the best experts on this subject based on the ideXlab platform.

  • an Additive Model as a physical basis for shadow fading
    IEEE Transactions on Vehicular Technology, 2007
    Co-Authors: J Salo, L Vuokko, H M Elsallabi, P Vainikainen
    Abstract:

    Received signal power in mobile wireless communications is typically Modeled as a product of three factors: distance-dependent average path loss law, variation in the local mean power (shadow fading), and small-scale fading. Of these three factors, the least investigated is the shadow fading, which is usually explained as a result of multiplication of large number of random attenuating factors in the radio channel. In this paper, the authors propose an Additive Model as an alternative physical basis for shadow fading within an “extended local area” where path loss is constant. Starting from a sum-of-sinusoids signal Model, they show that under mild statistical assumptions on the powers of the sinusoids, the resulting signal power will have approximately Gaussian distribution in logarithmic scale. A cluster-based Model for shadow fading emerges as a special instance of the general result. They present simulation and measurement results that support their theoretical findings. The new physical basis for shadow fading also provides insights into simulation and Modeling of radio channels

Mladen Kolar – One of the best experts on this subject based on the ideXlab platform.

  • kernel meets sieve post regularization confidence bands for sparse Additive Model
    Journal of the American Statistical Association, 2020
    Co-Authors: Mladen Kolar, Han Liu
    Abstract:

    We develop a novel procedure for constructing confidence bands for components of a sparse Additive Model. Our procedure is based on a new kernel-sieve hybrid estimator that combines two most popula…

  • Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model.
    arXiv: Machine Learning, 2015
    Co-Authors: Mladen Kolar, Han Liu
    Abstract:

    We develop a novel procedure for constructing confidence bands for components of a sparse Additive Model. Our procedure is based on a new kernel-sieve hybrid estimator that combines two most popular nonparametric estimation methods in the literature, the kernel regression and the spline method, and is of interest in its own right. Existing methods for fitting sparse Additive Model are primarily based on sieve estimators, while the literature on confidence bands for nonparametric Models are primarily based upon kernel or local polynomial estimators. Our kernel-sieve hybrid estimator combines the best of both worlds and allows us to provide a simple procedure for constructing confidence bands in high-dimensional sparse Additive Models. We prove that the confidence bands are asymptotically honest by studying approximation with a Gaussian process. Thorough numerical results on both synthetic data and real-world neuroscience data are provided to demonstrate the efficacy of the theory.