Results 41 to 50 of about 2,094,340 (297)

Logistic Regression Diagnostics

The Annals of Statistics, 1981
A maximum likelihood fit of a logistic regression model (and other similar models) is extremely sensitive to outlying responses and extreme points in the design space. We develop diagnostic measures to aid the analyst in detecting such observations and in quantifying their effect on various aspects of the maximum likelihood fit.
openaire   +3 more sources

Model Logistic Regression dalam Penentuan Kebijakan Dividen Perusahaan di Indonesia [PDF]

, 2012
This paper investagates dividend policy decision in Indonesian Stock Exchange (IDX) through studying non-financial firms. Panel data were obtained from 1490 non-financial firms over the five year period from 2006 to 2010, where 310 firms pay dividend and
Satmoko, A. (Agung), Sudarman, S. (Sudarman)   +1 more
core  

Predictors of response and rational combinations for the novel MCL‐1 inhibitor MIK665 in acute myeloid leukemia

Molecular Oncology, EarlyView.
This study characterizes the responses of primary acute myeloid leukemia (AML) patient samples to the MCL‐1 inhibitor MIK665. The results revealed that monocytic differentiation is associated with MIK665 sensitivity. Conversely, elevated ABCB1 expression is a potential biomarker of resistance to the treatment, which can be overcome by the combination ...
Joseph Saad, Rhiannon Newman, Elmira Khabusheva, Sofia Aakko, Eric Durand, Mahesh Tambe, Heikki Kuusanmäki, Alun Parsons, Juho J. Miettinen, Komal Kumar Javarappa, Ezgi June Olgac, Nemo Ikonen, Mika Kontro, Kimmo Porkka, Heiko Maacke, Janghee Woo, Ensar Halilovic, Caroline A. Heckman   +17 more
wiley   +1 more source

Expectation-maximization for logistic regression [PDF]

, 2013
We present a family of expectation-maximization (EM) algorithms for binary and negative-binomial logistic regression, drawing a sharp connection with the variational-Bayes algorithm of Jaakkola and Jordan (2000).
Scott, James G., Sun, Liang
core  

Next‐generation proteomics improves lung cancer risk prediction

Molecular Oncology, EarlyView.
This is one of very few studies that used prediagnostic blood samples from participants of two large population‐based cohorts. We identified, evaluated, and validated an innovative protein marker model that outperformed an established risk prediction model and criteria employed by low‐dose computed tomography in lung cancer screening trials.
Megha Bhardwaj, Clara Frick, Ben Schöttker, Bernd Holleczek, Hermann Brenner   +4 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

Logistic Regression Model Using Scheimpflug-Placido Cornea Topographer Parameters to Diagnose Keratoconus [PDF]

, 2021
Emre Altınkurt, Ozkan Avci, Orkun Müftüoğlu, Adem Uğurlu, Zafer Cebeci, Kemal Turgay Özbilen   +5 more
openalex   +1 more source

Structured Learning via Logistic Regression [PDF]

, 2014
A successful approach to structured learning is to write the learning objective as a joint function of linear parameters and inference messages, and iterate between updates to each.
Domke, Justin
core  

LDAcoop: Integrating non‐linear population dynamics into the analysis of clonogenic growth in vitro

Molecular Oncology, EarlyView.
Limiting dilution assays (LDAs) quantify clonogenic growth by seeding serial dilutions of cells and scoring wells for colony formation. The fraction of negative wells is plotted against cells seeded and analyzed using the non‐linear modeling of LDAcoop.
Nikko Brix, Daniel Samaga, Katharina Gehr, Benedek Dankó, Mohamed Schumann, Guido Drexler, Ahmed Alnatsha, Georg Beyer, Ujjwal Mahajan, Martin Selmansberger, Julia Mayerle, Claus Belka, Horst Zitzelsberger, Kirsten Lauber   +13 more
wiley   +1 more source

PEMODELAN RISIKO PENYAKIT PNEUMONIA PADA BALITA DI PROVINSI JAWA TIMUR DENGAN PENDEKATAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION

E-Jurnal Matematika, 2015
This research is aim to determine the comparison of logistic regression models and models Geographically Weighted Logistic Regression and the factors that significantly affect the risk of pneumonia in toddlers in East Java Province.
EVI NOVIYANTARI FATIMAH, I KOMANG GDE SUKARSA, MADE SUSILAWATI   +2 more
doaj   +1 more source