Results 41 to 50 of about 1,409,985 (359)

APPLICATION OF PENALIZED SPLINE-SPATIAL AUTOREGRESSIVE MODEL TO HIV CASE DATA IN INDONESIA

Barekeng, 2023
Spatial regression analysis is a statistical method used to perform modeling by considering spatial effects. Spatial models generally use a parametric approach by assuming a linear relationship between explanatory and response variables.
Nindi Pigitha, Anik Djuraidah, Aji Hamim Wigena   +2 more
doaj   +1 more source

A Computationally Efficient Projection-Based Approach for Spatial Generalized Linear Mixed Models [PDF]

Journal of Computational And Graphical Statistics, 2016
Inference for spatial generalized linear mixed models (SGLMMs) for high-dimensional non-Gaussian spatial data is computationally intensive. The computational challenge is due to the high-dimensional random effects and because Markov chain Monte Carlo ...
Yawen Guan, M. Haran
semanticscholar   +1 more source

An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group

Frontiers in Neuroinformatics, 2019
Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses.
Premika S. W. Boedhoe, Premika S. W. Boedhoe, Martijn W. Heymans, Lianne Schmaal, Lianne Schmaal, Yoshinari Abe, Pino Alonso, Pino Alonso, Pino Alonso, Stephanie H. Ameis, Stephanie H. Ameis, Alan Anticevic, Paul D. Arnold, Paul D. Arnold, Marcelo C. Batistuzzo, Francesco Benedetti, Jan C. Beucke, Irene Bollettini, Anushree Bose, Silvia Brem, Anna Calvo, Rosa Calvo, Rosa Calvo, Yuqi Cheng, Kang Ik K. Cho, Valentina Ciullo, Valentina Ciullo, Sara Dallaspezia, Damiaan Denys, Damiaan Denys, Jamie D. Feusner, Kate D. Fitzgerald, Jean-Paul Fouche, Egill A. Fridgeirsson, Patricia Gruner, Gregory L. Hanna, Derrek P. Hibar, Marcelo Q. Hoexter, Hao Hu, Chaim Huyser, Chaim Huyser, Neda Jahanshad, Anthony James, Norbert Kathmann, Christian Kaufmann, Kathrin Koch, Kathrin Koch, Jun Soo Kwon, Jun Soo Kwon, Luisa Lazaro, Luisa Lazaro, Luisa Lazaro, Luisa Lazaro, Christine Lochner, Rachel Marsh, Rachel Marsh, Ignacio Martínez-Zalacaín, David Mataix-Cols, José M. Menchón, José M. Menchón, José M. Menchón, Luciano Minuzzi, Astrid Morer, Astrid Morer, Astrid Morer, Takashi Nakamae, Tomohiro Nakao, Janardhanan C. Narayanaswamy, Seiji Nishida, Erika L. Nurmi, Joseph O'Neill, John Piacentini, Fabrizio Piras, Federica Piras, Y. C. Janardhan Reddy, Tim J. Reess, Tim J. Reess, Yuki Sakai, Yuki Sakai, Joao R. Sato, H. Blair Simpson, H. Blair Simpson, Noam Soreni, Carles Soriano-Mas, Carles Soriano-Mas, Carles Soriano-Mas, Gianfranco Spalletta, Gianfranco Spalletta, Michael C. Stevens, Michael C. Stevens, Philip R. Szeszko, Philip R. Szeszko, David F. Tolin, David F. Tolin, Guido A. van Wingen, Ganesan Venkatasubramanian, Susanne Walitza, Zhen Wang, Zhen Wang, Je-Yeon Yun, Je-Yeon Yun, ENIGMA-OCD Working-Group, Paul M. Thompson, Dan J. Stein, Odile A. van den Heuvel, Odile A. van den Heuvel, Jos W. R. Twisk   +106 more
doaj   +1 more source

Comparison of regression-based and machine learning techniques to explain alpha diversity of fish communities in streams of central and eastern India

Ecological Indicators, 2021
Over the past several decades, ecologists have been striving to develop models that accurately describe species-habitat relationships across ecological communities.
Rubina Mondal, Anuradha Bhat
doaj   +1 more source

Reducing Selection Bias in Analyzing Longitudinal Health Data with High Mortality Rates [PDF]

, 2010
Two longitudinal regression models, one parametric and one nonparametric, are developed to reduce selection bias when analyzing longitudinal health data with high mortality rates.
Engel, Charles C., Gore, Kristie L., Kang, Han, Liu, Xian   +3 more
core   +2 more sources

Minimization of Akaike's information criterion in linear regression analysis via mixed integer nonlinear program [PDF]

Optim. Methods Softw., 2016
Akaike's information criterion (AIC) is a measure of evaluating statistical models for a given data set. We can determine the best statistical model for a particular data set by finding the model with the smallest AIC value. Since there are exponentially
K. Kimura, Hayato Waki
semanticscholar   +1 more source

Optimal Antibody Purification Strategies Using Data-Driven Models

Engineering, 2019
This work addresses the multiscale optimization of the purification processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of ...
Songsong Liu, Lazaros G. Papageorgiou
doaj   +1 more source

Additive quantile mixed effects modelling with application to longitudinal CD4 count data

Scientific Reports, 2021
Quantile regression offers an invaluable tool to discern effects that would be missed by other conventional regression models, which are solely based on modeling conditional mean.
Ashenafi A. Yirga, Sileshi F. Melesse, Henry G. Mwambi, Dawit G. Ayele   +3 more
doaj   +1 more source

The development of a simple basal area increment model [PDF]

, 2011
In most cases forest practice in Austria use yield tables to predict the growth of their forests. Common yield tables show the increment of pure even-aged stands which are treated in a way the table developer recommends.
Georg Erich Kindermann
core   +2 more sources

Statistical primer: an introduction to the application of linear mixed-effects models in cardiothoracic surgery outcomes research—a case study using homograft pulmonary valve replacement data

European Journal of Cardio-Thoracic Surgery, 2022
Summary OBJECTIVES The emergence of big cardio-thoracic surgery datasets that include not only short-term and long-term discrete outcomes but also repeated measurements over time offers the opportunity to apply more advanced modelling of outcomes.
Xu Wang, E. Andrinopoulou, K. Veen, A. Bogers, J. Takkenberg   +4 more
semanticscholar   +1 more source