Results 231 to 240 of about 354,947 (289)

Fine-grained causal effect estimation of learning resources via dynamic causal heterogeneous graph neural networks. [PDF]

Sci Rep
Ren Y, Chen Z, Jiang X, Du Z.
europepmc   +1 more source

Geographically weighted Weibull regression modeling on dissolved oxygen data to analyze river water quality in East Kalimantan. [PDF]

MethodsX
Suyitno S, Darnah, Hayati MN, Dani ATR, Purnamasari I, Goejantoro R, Siringoringo M, Nugraha PY, Fauziyah M, Fauziyah ZN, Mislan.   +10 more
europepmc   +1 more source

A comparison of Kaplan-Meier-based inverse probability of censoring weighted regression methods. [PDF]

Lifetime Data Anal
Overgaard M.
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Enhanced log ratio calibration methods for stratified variance estimation in survey sampling. [PDF]

Sci Rep
Zaka A, Alghamdi FM, Alsheikh SMA, Jabeen R, Minhas KS, Bano S, Afify AZ.   +6 more
europepmc   +1 more source

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Boosting Regression Estimators

Neural Computation, 1999
There is interest in extending the boosting algorithm (Schapire, 1990) to fit a wide range of regression problems. The threshold-based boosting algorithm for regression used an analogy between classification errors and big errors in regression. We focus on the practical aspects of this algorithm and compare it to other attempts to extend boosting to ...
R, Avnimelech, N, Intrator
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Regression Estimation in Simulation

Journal of the Operational Research Society, 1980
In simulation an input variable like interarrival time is sampled, and hence its average deviates from its known expectation. This information can be used to improve the estimated simulation response: regression sampling or control variate technique. The usual crude estimator is shown to be biased.
Hopmans, Anton C. M., Kleijnen, Jack P. C.   +1 more
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Multiple Model Regression Estimation

IEEE Transactions on Neural Networks, 2005
This paper presents a new learning formulation for multiple model estimation (MME). Under this formulation, training data samples are generated by several (unknown) statistical models. Hence, most existing learning methods (for classification or regression) based on a single model formulation are no longer applicable.
Vladimir, Cherkassky, Yunqian, Ma
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