Simultaneous dimension reduction and adjustment for confounding variation

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Simultaneous dimension reduction and adjustment for confounding variation.

Dimension reduction methods are commonly applied to high-throughput biological datasets. However, the results can be hindered by confounding factors, either biological or technical in origin. In this study, we extend principal component analysis (PCA) to propose AC-PCA for simultaneous dimension reduction and adjustment for confounding (AC) variation. We show that AC-PCA can adjust for (i) vari...

متن کامل

Model Based Approaches for Simultaneous Dimension Reduction and Clustering

High dimensional data are regularly generated from various sources. Thus subsets of data are usually concentrated in different subspaces. It is of interest to develop methods that achieve simultaneous dimension reduction and clustering. In this talk we will present a new solution based on a constrained version of mixture of factor analyzers. Our proposed technique imposes constraints on the und...

متن کامل

Process variation dimension reduction based on SVD

We propose an algorithm based on singular value decomposition (SVD) to reduce the number of process variation variables. With few process variation variables, fault simulation and timing analysis under process variation can be performed efficiently. Our algorithm reduces the number of process variation variables while preserving the delay function with respect to process variation. Compared wit...

متن کامل

Dimension reduction and alleviation of confounding for spatial generalized linear mixed models

Non-Gaussian spatial data are very common in many disciplines.For instance, count data are common in disease mapping, and binary data are common in ecology.When fitting spatial regressions for such data, one needs to account for dependence to ensure reliable inference for the regression coefficients. The spatial generalized linear mixed model offers a very popular and flexible approach to model...

متن کامل

Generalized Adjustment Under Confounding and Selection Biases

Selection and confounding biases are the two most common impediments to the applicability of causal inference methods in large-scale settings. We generalize the notion of backdoor adjustment to account for both biases and leverage external data that may be available without selection bias (e.g., data from census). We introduce the notion of adjustment pair and present complete graphical conditi...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Proceedings of the National Academy of Sciences

سال: 2016

ISSN: 0027-8424,1091-6490

DOI: 10.1073/pnas.1617317113