Brain tumor detection and patient survival prediction using U‐Net and regression model
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Imaging Systems and Technology"
DOI: 10.1002/ima.22735
Abstract: Brain tumor segmentation is necessitated to ascertain the severity of tumor growth in a brain for possible treatment planning. In this work, we attempt the development of U‐Net‐based semantic segmentation of brain tumors. This network… read more here.
Keywords: tumor; brain tumor; model; regression model ... See more keywords
Ultrasound Radiomics‐Based Logistic Regression Model to Differentiate Between Benign and Malignant Breast Nodules
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Ultrasound in Medicine"
DOI: 10.1002/jum.16078
Abstract: To explore the potential value of ultrasound radiomics in differentiating between benign and malignant breast nodules by extracting the radiomic features of two‐dimensional (2D) grayscale ultrasound images and establishing a logistic regression model. read more here.
Keywords: malignant breast; logistic regression; breast nodules; ultrasound radiomics ... See more keywords
“Improving the performance of Bayesian logistic regression model with overdose control in oncology dose‐finding studies” by Hongtao Zhang, Alan Chiang, and Jixian Wang
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in Medicine"
DOI: 10.1002/sim.9494
Abstract: In their paper, Zhang et al 1 propose further extensions of the Bayesian Logistic Regression Model (BLRM) with overdose control for dose-escalation studies of a novel drug. These extensions aim to reduce the risk of… read more here.
Keywords: oncology; logistic regression; zhang; overdose control ... See more keywords
Commentary on “Improving the performance of Bayesian logistic regression model with overdose control in oncology dose‐finding studies”
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in Medicine"
DOI: 10.1002/sim.9496
Abstract: The Bayesian logistic regression model (BLRM) design is a variation of the continuous reassessment method (CRM). Due to the use of an excessively tight escalation with overdose control (EWOC) rule, BLRM has high tendency to… read more here.
Keywords: oncology; logistic regression; overdose control; bayesian logistic ... See more keywords
Unified estimation for Cox regression model with nonmonotone missing at random covariates
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DOI: 10.1002/sim.9512
Abstract: This article investigates a unified estimator for Cox regression model (Cox, 1972) when covariate data are missing at random (Rubin, 1976). It extends the idea of using parametric working models (Zhao and Liu, 2021) to… read more here.
Keywords: cox regression; regression model; cox; unified estimator ... See more keywords
A Bayesian (meta‐)regression model for treatment effects on the risk difference scale
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DOI: 10.1002/sim.9697
Abstract: In clinical settings, the absolute risk reduction due to treatment that can be expected in a particular patient is of key interest. However, logistic regression, the default regression model for trials with a binary outcome,… read more here.
Keywords: treatment effects; meta; model; treatment ... See more keywords
Statistical inference for the functional quadratic quantile regression model
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DOI: 10.1007/s00184-020-00763-5
Abstract: In this paper, we develop statistical inference procedures for functional quadratic quantile regression model in which the response is a scalar and the predictor is a random function defined on a compact set of R… read more here.
Keywords: quantile regression; regression model; functional quadratic; model ... See more keywords
Detecting over- and under-dispersion in zero inflated data with the hyper-Poisson regression model
Sign Up to like & getrecommendations! Published in 2017 at "Statistical Papers"
DOI: 10.1007/s00362-015-0683-1
Abstract: The zero inflated hyper-Poisson regression model permits count data to be analysed with covariates that determine different levels of dispersion and that present structural zeros due to the existence of a non-users group. A simulation… read more here.
Keywords: regression model; zero inflated; model; dispersion ... See more keywords
A regression model for overdispersed data without too many zeros
Sign Up to like & getrecommendations! Published in 2017 at "Statistical Papers"
DOI: 10.1007/s00362-015-0724-9
Abstract: A regression model for overdispersed count data based on the complex biparametric Pearson (CBP) distribution is developed. It is compared with the generalized Poisson regression model, the negative binomial regression model and the zero inflated… read more here.
Keywords: cbp distribution; model overdispersed; regression; regression model ... See more keywords
Complete consistency of estimators for regression models based on extended negatively dependent errors
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DOI: 10.1007/s00362-016-0771-x
Abstract: In this paper, we investigate the consistency of the estimators of nonparametric regression model and multiple linear regression model based on extended negatively dependent errors. The complete convergence rates of the estimators of nonparametric regression… read more here.
Keywords: extended negatively; regression model; consistency; consistency estimators ... See more keywords
Uncertain Gompertz regression model with imprecise observations
Sign Up to like & getrecommendations! Published in 2020 at "Soft Computing"
DOI: 10.1007/s00500-018-3611-1
Abstract: Regression is widely applied in many fields. Regardless of the types of regression, we often assume that the observations are precise. However, in real-life circumstances, this assumption can only be met sometimes, which means the… read more here.
Keywords: regression; gompertz regression; imprecise; uncertain gompertz ... See more keywords