Results 81 to 90 of about 6,937 (202)

Censoring, Competing Events, and Multistate Models: Comment on Beyersmann et al. “Hazards Constitute Key Quantities for Analyzing, Interpreting and Understanding Time‐to‐Event Data”

Biometrical Journal, Volume 68, Issue 4, August 2026.
ABSTRACT Beyersmann et al. propose a functional interpretation of hazards, viewing them as evolving quantities describing the entire event process rather than as pointwise causal contrasts. In this commentary, we elaborate on the implications of this view for causal inference in modern clinical trials with survival outcomes. We emphasize how censoring,
Malka Gorfine, Daniel Nevo
wiley   +1 more source

Overlap‐Weight Estimators With Machine‐Learned Plug‐Ins and Overlap‐Weight Targeted Maximum Likelihood Estimator

Biometrical Journal, Volume 68, Issue 4, August 2026.
ABSTRACT In estimating the average treatment effect (ATE), the plug‐in estimator with the efficient influence function and the targeted maximum likelihood estimator (TMLE) are semiparametrically efficient. However, the estimators suffer from the fact that too small/large a propensity score (PS) in the denominators can make the estimators unstable. This
Myoung‐jae Lee, Sanghyeok Lee
wiley   +1 more source

THE PARAMETRIC AND NONPARAMETRIC ESTIMATOR IN SEMIPARAMETRIC REGRESSION FOR LONGITUDINAL DATA WITH SPLINE APPROACH

Jurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi, 2023
Regression analysis aims to determine the relationship between response variables and predictor variables. There are three approaches to estimate regression curves, there are parametric, nonparametric, and semiparametric regression.
Tony Yulianto, Kuzairi Kuzairi, Noer Azizah, M. Fariz Fadillah Mardianto, Ira Yuditira, Faisol Faisol, Rica Amalia   +6 more
doaj  

Minimum capital requirement and portfolio allocation for non-life insurance: a semiparametric model with Conditional Value-at-Risk (CVaR) constraint. [PDF]

Comput Manag Sci, 2023
Staino A, Russo E, Costabile M, Leccadito A.   +3 more
europepmc   +1 more source

Improving the Finite Sample Estimation of Average Treatment Effects Using Double/Debiased Machine Learning With Propensity Score Calibration

Journal of Applied Econometrics, Volume 41, Issue 5, Page 613-626, August 2026.
ABSTRACT Double/debiased machine learning (DML) uses for estimating an average treatment effect (ATE) a double‐robust score function that relies on the prediction of nuisance functions, such as the propensity score, which is the probability of treatment assignment given covariates.
Daniele Ballinari, Nora Bearth
wiley   +1 more source

Semiparametric volatility model with varying frequencies

Communications in Statistics - Simulation and Computation
In extracting time series data from various sources, it is inevitable to compile variables measured at varying frequencies as this is often dependent on the source. Modeling from these data can be facilitated by aggregating high frequency data to match the relatively lower frequencies of the rest of the variables.
Jetrei Benedick R. Benito, Joseph Ryan G. Lansangan, Erniel B. Barrios   +2 more
openaire   +2 more sources

Model Averaging with AIC Weights for Hypothesis Testing of Hormesis at Low Doses

Dose-Response, 2017
For many dose–response studies, large samples are not available. Particularly, when the outcome of interest is binary rather than continuous, a large sample size is required to provide evidence for hormesis at low doses. In a small or moderate sample, we
Steven B. Kim, Nathan Sanders
doaj   +1 more source

A semiparametric model for between-subject attributes: Applications to beta-diversity of microbiome data. [PDF]

Biometrics, 2022
Liu J, Zhang X, Chen T, Wu T, Lin T, Jiang L, Lang S, Liu L, Natarajan L, Tu JX, Kosciolek T, Morton J, Nguyen TT, Schnabl B, Knight R, Feng C, Zhong Y, Tu XM.   +17 more
europepmc   +1 more source

Inverse‐Intensity‐Weighted Generalized Estimating Equations With Irregularly Measured Longitudinal Data and Informative Dropout

Statistics in Medicine, Volume 45, Issue 18-19, August 2026.
ABSTRACT Longitudinal data are commonly encountered in biomedical research, including randomized trials and retrospective cohort studies. Subjects are typically followed over a period of time and may be scheduled for follow‐up at predetermined time points.
George Stefan, Eleanor Pullenayegum
wiley   +1 more source

Quantile estimation of semiparametric model with time-varying coefficients for panel count data. [PDF]

PLoS One, 2021
Wang Y, Wang W.
europepmc   +1 more source