Results 41 to 50 of about 172,938 (287)
Average Derivative Estimation from Biased Data [PDF]
We investigate the estimation of the density-weighted average derivative from biased data. An estimator integrating a plug-in approach and wavelet projections is constructed. We prove that it attains the parametric rate of convergence 1/n under the mean squared error.
Chesneau, Christophe +2 more
openaire +3 more sources
On the biased Two-Parameter Estimator to Combat Multicollinearity in Linear Regression Model
The most popularly used estimator to estimate the regression parameters in the linear regression model is the ordinary least-squares (OLS). The existence of multicollinearity in the model renders OLS inefficient.
Janet Iyabo Idowu +3 more
doaj +1 more source
Sickle Cell Disease Is an Inherent Risk for Asthma in a Sibling Comparison Study
ABSTRACT Introduction Sickle cell disease (SCD) and asthma share a complex relationship. Although estimates vary, asthma prevalence in children with SCD is believed to be comparable to or higher than the general population. Determining whether SCD confers an increased risk for asthma remains challenging due to overlapping symptoms and the ...
Suhei C. Zuleta De Bernardis +9 more
wiley +1 more source
Poisson regression is used to model count response variables. The method has a strict assumption that the mean and variance of the response variable are equal, while, in practice, the case of overdispersion is common.
Rasha A. Farghali +4 more
doaj +1 more source
Estimators in simple random sampling: Searls approach [PDF]
This paper investigates four new estimators in simple random sampling: biased sample mean, ratio estimator and two linear regression estimators, using known coefficients of variation of the study variable and auxiliary variable. The properties of the new
Jirawan Jitthavech +1 more
doaj
On Biased Stochastic Gradient Estimation
We present a uniform analysis of biased stochastic gradient methods for minimizing convex, strongly convex, and non-convex composite objectives, and identify settings where bias is useful in stochastic gradient estimation. The framework we present allows us to extend proximal support to biased algorithms, including SAG and SARAH, for the first time in ...
Driggs, Derek +2 more
openaire +3 more sources
Psychosocial Outcomes in Patients With Endocrine Tumor Syndromes: A Systematic Review
ABSTRACT Introduction The combination of disease manifestations, the familial burden, and varying penetrance of endocrine tumor syndromes (ETSs) is unique. This review aimed to portray and summarize available data on psychosocial outcomes in patients with ETSs and explore gaps and opportunities for future research and care.
Daniël Zwerus +6 more
wiley +1 more source
Bias in Stabilized Sieve Sampling [PDF]
The stabilized sieve sample selection method (SSM) is considered to be a probability proportional to size (PPS) sampling method with an unbiased estimator (Horgan 1997, 1998). This article demonstrates that SSM does not select items with PPS and that the
Guan, Liming, Wendell, John P.
core +2 more sources
Lifestyle Behaviors and Cardiotoxic Treatment Risks in Adult Childhood Cancer Survivors
ABSTRACT Background Higher doses of anthracyclines and heart‐relevant radiotherapy increase cardiovascular disease (CVD) risk. This study assessed CVD and CVD risk factors among adult childhood cancer survivors (CCSs) across cardiotoxic treatment risk groups and examined associations between lifestyle behaviors and treatment risks.
Ruijie Li +6 more
wiley +1 more source
Biased Versus Unbiased Estimation
AbstractStatisticians have begun to realize that certain deliberately induced biases can dramatically improve estimation properties when there are several parameters to be estimated. This represents a radical departure from the tradition of unbiased estimation which has dominated statistical thinking since the work of Gauss. We briefly describe the new
openaire +2 more sources

