Results 91 to 100 of about 2,031,080 (289)
Decision-Directed Recursive Least Squares MIMO Channels Tracking
EURASIP Journal on Wireless Communications and Networking, 2006 A new approach for joint data estimation and channel tracking for multiple-input multiple-output (MIMO) channels is proposed based on the decision-directed recursive least squares (DD-RLS) algorithm.
Karami Ebrahim, Shiva Mohsen
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Least squares approximations [PDF]
, 1962 Thesis (M.A.)--Boston UniversityThis paper, utilizing the properties of Vector spaces, describes an approach to polynomial approximations of functions defined analytically or by a set of observations over some interval.
Wiener, Marvin
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Molecular Oncology, EarlyView.Radiotherapy (RT) response depends on the DNA repair capacity of tumor and host cells. We show that circulating tumor cell (CTC) counts and apoptosis rates before and after RT predict treatment response and outcome, which can be accessed via easily accessible liquid biopsy approaches. Created in BioRender. Wikman, H.
Yvonne Goy +10 more
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Molecular Oncology, EarlyView.A urine‐based digital PCR assay targeting two hotspot TERT promoter variants detected bladder cancer with high sensitivity and no false positives in this case–control cohort. The streamlined AbsoluteQ workflow outperformed Sanger sequencing and supports non‐invasive molecular testing for bladder cancer detection.
Anna Nykel +12 more
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Least Squares Problems with Absolute Quadratic Constraints
Journal of Applied Mathematics, 2012 This paper analyzes linear least squares problems with absolute quadratic constraints. We develop a generalized theory following Bookstein's conic-fitting and Fitzgibbon's direct ellipse-specific fitting.
R. Schöne, T. Hanning
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Least-Squares Means: The R Package lsmeans
Journal of Statistical Software, 2016 Least-squares means are predictions from a linear model, or averages thereof. They are useful in the analysis of experimental data for summarizing the effects of factors, and for testing linear contrasts among predictions. The lsmeans package (Lenth 2016)
Russell V. Lenth
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Sparse least trimmed squares regression. [PDF]
Sparse model estimation is a topic of high importance in modern data analysis due to the increasing availability of data sets with a large number of variables. Another common problem in applied statistics is the presence of outliers in the data.
Alfons, Andreas +2 more
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Least Squares Estimation Principle and its Geometrical Interpretation [PDF]
, 1990 pdf contains 14 ...
Lashkari, Khosrow
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FEBS Open Bio, EarlyView.CRISPRI‐mediated gene silencing and phenotypic exploration in nontuberculous mycobacteria. In this Research Protocol, we describe approaches to control, monitor, and quantitatively assess CRISPRI‐mediated gene silencing in M. smegmatis and M. abscessus model organisms.
Vanessa Point +7 more
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Partial Least Squares Regression for Binary Data
MathematicsClassical Partial Least Squares Regression (PLSR) models were developed primarily for continuous data, allowing dimensionality reduction while preserving relationships between predictors and responses. However, their application to binary data is limited.
Laura Vicente-Gonzalez +2 more
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