Results 61 to 70 of about 12,380,363 (249)
, 2004 A large amount of data collected in the social sciences are counts crossclassified into categories. These counts are non-negative integers and require special methods of analysis to model appropriately; log-linear models are one sophisticated method. The
Dunn, Peter K.
core
Pediatric Blood &Cancer, EarlyView.ABSTRACT Purpose To explore caregivers' experiences and perceptions of the nutritional challenges faced by children and adolescents undergoing cancer treatment, including oral intake and the use of nutritional support. Methods This was a qualitative study based on focus groups with parents and caregivers at a Brazilian pediatric oncology hospital ...
Danyelle Apparecida Balsamão Marques +5 more
wiley +1 more source
Sparse nested Markov models with log-linear parameters
, 2013 Hidden variables are ubiquitous in practical data analysis, and therefore modeling marginal densities and doing inference with the resulting models is an important problem in statistics, machine learning, and causal inference.
Robins, James +3 more
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Data Science in ScienceRidge, Liu, and Kibria–Lukman regression methods that have been proposed to solve the multicollinearity problem for both linear and generalized linear regression models (Kibria and Lukman, Shewa and Ugwuowo). This paper considers several different ridge,
Sergio Perez Melo +3 more
doaj +1 more source
Total ozone time series analysis: a neural network model approach [PDF]
Nonlinear Processes in Geophysics, 2004 This work is focused on the application of neural network based models to the analysis of total ozone (TO) time series. Processes that affect total ozone are extremely non linear, especially at the considered European mid-latitudes.
B. M. Monge Sanz
doaj
Regularization Methods Based on the Lq-Likelihood for Linear Models with Heavy-Tailed Errors
Entropy, 2020 We propose regularization methods for linear models based on the Lq-likelihood, which is a generalization of the log-likelihood using a power function. Regularization methods are popular for the estimation in the normal linear model.
Yoshihiro Hirose
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Forecasting telecommunications data with linear models [PDF]
For telecommunication companies to successfully manage their business, companies rely on mapping future trends and usage patterns. However, the evolution of telecommunications technology and systems in the provision of services renders imperfections in ...
Tan, Joachim, Madden, Gary G
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Statistics: general linear models (a flexible approach)
, 2014 This article moves on to discuss a type of statistical testing different from those we have discussed previously, namely a General Linear Model. This system incorporates a number of other statistical models and is a powerful tool used widely in modern ...
Flaherty, D., Scott, M., Currall, J.
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Pediatric Blood &Cancer, EarlyView.ABSTRACT The medical burdens of sickle cell disease (SCD) and transfusion‐dependent β‐thalassemia (TDT) are chronic and cumulative, even in children. Yet the effects expand beyond medications and healthcare visits due to their impacts on school attendance, academic performance, and peer engagement.
Mary Ann Etling +2 more
wiley +1 more source
Methods in Ecology and EvolutionBiological data often violate the assumption of constant variance, yet such heteroscedasticity can reflect meaningful biological processes such as plasticity, canalization or stress responses. Despite this, most models treat variance as statistical noise.
Shinichi Nakagawa +6 more
doaj +1 more source