نتایج جستجو برای: روش انقباضی lasso

تعداد نتایج: 374444  

2007
Samiran Ghosh

Lasso proved to be an extremely successful technique for simultaneous estimation and variable selection. However lasso has two major drawbacks. First, it does not capture any grouping effect and secondly in some situations lasso solutions are inconsistent. To overcome inconsistency recently adaptive lasso was proposed where adaptive weights are used for penalizing different coefficients. Adapti...

2016
Hadi Raeisi Shahraki Saeedeh Pourahmad Seyyed Mohammad Taghi Ayatollahi

Despite the widespread use of liver transplantation as a routine therapy in liver diseases, the effective factors on its outcomes are still controversial. This study attempted to identify the most effective factors on death after liver transplantation. For this purpose, modified least absolute shrinkage and selection operator (LASSO), called Adaptive LASSO, was utilized. One of the best advanta...

ژورنال: طب توانبخشی 2014

مقدمه و اهداف پردازش دوگوشی یکی از توانایی های مهم شنوایی انسان است که در درک گفتار در محیط های نویزی نقش اساسی دارد. مؤلفه ی تعامل دوگوشی در انسان به عنوان شاخصی برای ارزیابی پردازش دوگوشی بکار می رود. در این تحقیق برای شناخت بهتر این پدیده در سطح شنوایی ساقه مغز و بررسی این که در کدام پلاریته پاسخ بهتری قابل ثبت است ، مؤلفه ی تعامل دو گوشی پاسخ شنوایی ساقه مغز در پلاریته های مختلف در افراد ط...

2009
Gina M D'Angelo DC Rao C Charles Gu

Variable selection in genome-wide association studies can be a daunting task and statistically challenging because there are more variables than subjects. We propose an approach that uses principal-component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) to identify gene-gene interaction in genome-wide association studies. A PCA was used to first reduce the dimension...

2008
GARETH M. JAMES PETER RADCHENKO JINCHI LV

We propose a new algorithm, DASSO, for fitting the entire coefficient path of the Dantzig selector with a similar computational cost to the LARS algorithm that is used to compute the Lasso. DASSO efficiently constructs a piecewise linear path through a sequential simplex-like algorithm, which is remarkably similar to LARS. Comparison of the two algorithms sheds new light on the question of how ...

2013
Ein Oh Tae Keun Yoo Eun-Cheol Park

BACKGROUND Blindness due to diabetic retinopathy (DR) is the major disability in diabetic patients. Although early management has shown to prevent vision loss, diabetic patients have a low rate of routine ophthalmologic examination. Hence, we developed and validated sparse learning models with the aim of identifying the risk of DR in diabetic patients. METHODS Health records from the Korea Na...

2008
Anne Dallas Svetlana V. Balatskaya Tai-Chih Kuo Heini Ilves Alexander V. Vlassov Roger L. Kaspar Kevin O. Kisich Sergei A. Kazakov Brian H. Johnston

We have developed a novel class of antisense agents, RNA Lassos, which are capable of binding to and circularizing around complementary target RNAs. The RNA Lasso consists of a fixed sequence derived from the hairpin ribozyme and an antisense segment whose size and sequence can be varied to base pair with accessible sites in the target RNA. The ribozyme catalyzes self-processing of the 5'- and ...

Journal: :CoRR 2016
Niharika Gauraha Swapan K. Parui

In this paper, we introduce Adaptive Cluster Lasso(ACL) method for variable selection in high dimensional sparse regression models with strongly correlated variables. To handle correlated variables, the concept of clustering or grouping variables and then pursuing model fitting is widely accepted. When the dimension is very high, finding an appropriate group structure is as difficult as the ori...

2008
PETER RADCHENKO GARETH M. JAMES

The Lasso is a popular and computationally efficient procedure for automatically performing both variable selection and coefficient shrinkage on linear regression models. One limitation of the Lasso is that the same tuning parameter is used for both variable selection and shrinkage. As a result, it typically ends up selecting a model with too many variables to prevent over shrinkage of the regr...

Journal: :Jurnal Natural 2023

A variable selection method is required to deal with regression models many variables, and LASSO has been the most widely used methodology. However, as several authors have noted, sensitive outliers in data. For this reason, Robust-LASSO approach was introduced by applying some weighting schemes for each sample This research presented a comparative study of three Robust LASSO, namely Huber-LASS...

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