Results 51 to 60 of about 71,763 (309)
Regression Designs in Autoregressive Stochastic Processes
The Annals of Statistics, 1974 This paper extends some recent results on asymptotically optimal sequences of experimental designs for regression problems in stochastic processes. In the regression model $Y(t) = \beta f(t) + X(t), 0 \leqq t \leqq 1$, the constant $\beta$ is to be estimated based on observations of $Y(t)$ and its first $m - 1$ derivatives at each of a set $T_n$ of $n$
Hajek, Jaroslav, Kimeldorf, George
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Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
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Journal of Applied Mathematics, 2014 The stochastic restricted r-k class estimator and stochastic restricted r-d class estimator are proposed for the vector of parameters in a multiple linear regression model with stochastic linear restrictions. The mean squared error matrix of the proposed
Jibo Wu
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Accounting for measurement error in log regression models with applications to accelerated testing. [PDF]
PLoS ONE, 2018 In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals.
Robert Richardson +3 more
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Molecular Oncology, EarlyView.Pair‐wise comparison of the CellSearch and FETCH enrichment technologies for circulating tumor cells (CTCs) from metastatic breast, prostate, and small cell lung cancer patients shows an increased capture of CTCs using FETCH enrichment. The clinical implementation of circulating tumor cells (CTCs) as a predictive tool for therapy efficacy in the ...
Michiel Stevens +6 more
wiley +1 more source
A Stochastic Restricted Principal Components Regression Estimator in the Linear Model
The Scientific World Journal, 2014 We propose a new estimator to combat the multicollinearity in the linear model when there are stochastic linear restrictions on the regression coefficients.
Daojiang He, Yan Wu
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Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
Strong consistency of Bayes estimates in nonlinear stochastic regression models [PDF]
, 1998 A broad range of nonlinear (linear) time series and stochastic processes can be described by the stochastic regression model yn-rn(0) + εn, where {εn} are independent random disturbances and rn is a random function of an unknown parameter 0 measurable ...
Hu, Inchi
core
Wavelets for Nonparametric Stochastic Regression with Mixing Stochastic Process [PDF]
Communications in Statistics - Theory and Methods, 2008 We propose a wavelet based stochastic regression function estimator for the estimation of the regression function for a sequence of mixing stochastic process with a common one-dimensional probability density function. Some asymptotic properties of the proposed estimator are investigated.
H. Doosti, M. Afshari, H. A. Niroumand
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Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source