Results 41 to 50 of about 2,178,475 (325)

Sparse Multinomial Logistic Regression via Approximate Message Passing

, 2016
For the problem of multi-class linear classification and feature selection, we propose approximate message passing approaches to sparse multinomial logistic regression (MLR).
Byrne, Evan, Schniter, Philip
core   +1 more source

Mycobacterial cell division arrest and smooth‐to‐rough envelope transition using CRISPRi‐mediated genetic repression systems

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, Wafaa Achache, Janïs Laudouze, Eliana Sepulveda Ramos, Mickaël Maziero, Céline Crauste, Stéphane Canaan, Pierre Santucci   +7 more
wiley   +1 more source

Robust logistic regression for insurance risk classification [PDF]

, 2001
Risk classification is an important part of the actuarial process in Insurance companies. It allows for the underwriting of the best risks, through an appropriate choice of classification variables, and helps set fair premiums in rate-making.
Flores, Esteban, Garrido, José
core   +1 more source

The sign of the logistic regression coefficient

, 2014
Let Y be a binary random variable and X a scalar. Let $\hat\beta$ be the maximum likelihood estimate of the slope in a logistic regression of Y on X with intercept.
Owen, Art B., Roediger, Paul A.
core   +1 more source

Identifying gene expression signatures for risk stratification of postoperative adjuvant chemotherapy in colorectal cancer

FEBS Open Bio, EarlyView.
A novel signature integrating genome‐wide analysis with clinical factors predicts recurrence in stage II colorectal cancer and enables a new risk stratification to guide postoperative adjuvant chemotherapy. Clinical risk stratification for postoperative recurrence in patients with pathological stage II (pStage II) colorectal cancer (CRC) is essential ...
Mayuko Otomo, Keisuke Okuno, Shuichi Watanabe, Sakiko Oba, Hiroyasu Kagawa, Masanori Tokunaga, Daisuke Ban, Yusuke Kinugasa   +7 more
wiley   +1 more source

Group Logistic Regression Models with l_p,q Regularization

Mathematics, 2022
In this paper, we proposed a logistic regression model with lp,q regularization that could give a group sparse solution. The model could be applied to variable-selection problems with sparse group structures. In the context of big data, the solutions for
Yanfang Zhang, Chuanhua Wei, Xiaolin Liu
doaj   +1 more source

Resampling Logistic Regression Untuk Penanganan Ketidakseimbangan Class Pada Prediksi Cacat Software [PDF]

, 2015
Software yang berkualitas tinggi adalah software yang dapat membantu proses bisnis Perusahaan dengan efektif, efesien dan tidak ditemukan cacat selama proses pengujian, pemeriksaan, dan implementasi.
Rianto, H. (Harsih), Wahono, R. S. (Romi)   +1 more
core  

Risk Prediction Models for Recurrence After Curative Treatment of Early‐Stage or Locally Advanced Lung Cancer: A Systematic Review

Aging and Cancer, EarlyView.
This systematic review synthesizes prognostic models for survival and recurrence in resected non‐small cell lung cancer. While many models demonstrate moderate to good discrimination, few are externally validated and reporting quality is variable, limiting clinical applicability and highlighting the need for robust, transparent model development ...
Evangeline Samuel, S. Tissera, E. Paul, J. Zalcberg, R. G. Stirling   +4 more
wiley   +1 more source

Deforestation modelling using logistic regression and GIS

Journal of Forest Science, 2015
A methodology has been used by means of which modellers and planners can quantify the certainty in predicting the location of deforestation. Geographic information system and logistic regression analyses were employed to predict the spatial distribution ...
M. Pir Bavaghar
doaj   +1 more source

Introduction to logistic regression

CoRR, 2020
For random field theory based multiple comparison corrections In brain imaging, it is often necessary to compute the distribution of the supremum of a random field. Unfortunately, computing the distribution of the supremum of the random field is not easy and requires satisfying many distributional assumptions that may not be true in real data.
openaire   +2 more sources