scholarly journals Approximate cross-validation formula for Bayesian linear regression

2016 ◽  
Author(s):  
Yoshiyuki Kabashima ◽  
Tomoyuki Obuchi ◽  
Makoto Uemura
Keyword(s):  
Linear Regression ◽  
Cross Validation ◽  
Bayesian Linear Regression

scholarly journals The forestecology R package for fitting and assessing neighborhood models of the effect of interspecific competition on the growth of trees

2021 ◽  
Author(s):  
Albert Kim ◽  
David Allen ◽  
Simon Couch
Keyword(s):  
Linear Regression ◽  
Interspecific Competition ◽  
Cross Validation ◽  
Model Performance ◽  
Global Network ◽  
Species Identity ◽  
Neighborhood Models ◽  
Competition Models ◽  
Bayesian Linear Regression ◽  
Neighborhood Competition

1. Neighborhood competition models are powerful tools to measure the effect of interspecific competition. Statistical methods to ease the application of these models are currently lacking. 2. We present the forestecology package providing methods to i) specify neighborhood competition models, ii) evaluate the effect of competitor species identity using permutation tests, and iii) measure model performance using spatial cross-validation. Following Allen (2020), we implement a Bayesian linear regression neighborhood competition model. 3. We demonstrate the package’s functionality using data from the Smithsonian Conservation Biology Institute’s large forest dynamics plot, part of the ForestGEO global network of research sites. Given ForestGEO’s data collection protocols and data formatting standards, the package was designed with cross-site compatibility in mind. We highlight the importance of spatial cross-validation when interpreting model results. 4. The package features i) tidyverse-like structure whereby verb-named functions can be modularly “piped” in sequence, ii) functions with standardized inputs/outputs of simple features ‘sf‘ package class, and iii) an S3 object-oriented implementation of the Bayesian linear regression model. These three facts allow for clear articulation of all the steps in the sequence of analysis and easy wrangling and visualization of the geospatial data. Furthermore, while the package only has Bayesian linear regression implemented, the package was designed with extensibility to other methods in mind.

Bayesian Linear Regression Model

2012 ◽  
Author(s):  
Biliana S. Güner ◽  
Svetlozar T. Rachev ◽  
John S. J. Hsu ◽  
Frank J. Fabozzi
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Bayesian Linear Regression

High Order Lambda Measure Based Choquet Integral Composition Forecasting Model

2013 ◽  
Vol 284-287 ◽  
pp. 3111-3114
Author(s):  
Hsiang Chuan Liu ◽  
Wei Sung Chen ◽  
Ben Chang Shia ◽  
Chia Chen Lee ◽  
Shang Ling Ou ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Cross Validation ◽  
Choquet Integral ◽  
Real Data ◽  
High Order ◽  
Forecasting Model ◽  
Mean Square ◽  
Weighted Composition ◽  
Fold Cross Validation

In this paper, a novel fuzzy measure, high order lambda measure, was proposed, based on the Choquet integral with respect to this new measure, a novel composition forecasting model which composed the GM(1,1) forecasting model, the time series model and the exponential smoothing model was also proposed. For evaluating the efficiency of this improved composition forecasting model, an experiment with a real data by using the 5 fold cross validation mean square error was conducted. The performances of Choquet integral composition forecasting model with the P-measure, Lambda-measure, L-measure and high order lambda measure, respectively, a ridge regression composition forecasting model and a multiple linear regression composition forecasting model and the traditional linear weighted composition forecasting model were compared. The experimental results showed that the Choquet integral composition forecasting model with respect to the high order lambda measure has the best performance.

scholarly journals Real-time estimation of daily physical activity intensity by a triaxial accelerometer and a gravity-removal classification algorithm

2011 ◽  
Vol 105 (11) ◽  
pp. 1681-1691 ◽  
Author(s):  
Kazunori Ohkawara ◽  
Yoshitake Oshima ◽  
Yuki Hikihara ◽  
Kazuko Ishikawa-Takata ◽  
Izumi Tabata ◽  
...  
Keyword(s):  
Physical Activity ◽  
Linear Regression ◽  
Cross Validation ◽  
Classification Algorithm ◽  
Daily Physical Activity ◽  
Triaxial Accelerometer ◽  
Validation Group ◽  
Activity Intensity ◽  
Metabolic Equivalents ◽  
Non Linear

We have recently developed a simple algorithm for the classification of household and locomotive activities using the ratio of unfiltered to filtered synthetic acceleration (gravity-removal physical activity classification algorithm, GRPACA) measured by a triaxial accelerometer. The purpose of the present study was to develop a new model for the immediate estimation of daily physical activity intensities using a triaxial accelerometer. A total of sixty-six subjects were randomly assigned into validation (n 44) and cross-validation (n 22) groups. All subjects performed fourteen activities while wearing a triaxial accelerometer in a controlled laboratory setting. During each activity, energy expenditure was measured by indirect calorimetry, and physical activity intensities were expressed as metabolic equivalents (MET). The validation group displayed strong relationships between measured MET and filtered synthetic accelerations for household (r 0·907, P < 0·001) and locomotive (r 0·961, P < 0·001) activities. In the cross-validation group, two GRPACA-based linear regression models provided highly accurate MET estimation for household and locomotive activities. Results were similar when equations were developed by non-linear regression or sex-specific linear or non-linear regressions. Sedentary activities were also accurately estimated by the specific linear regression classified from other activity counts. Therefore, the use of a triaxial accelerometer in combination with a GRPACA permits more accurate and immediate estimation of daily physical activity intensities, compared with previously reported cut-off classification models. This method may be useful for field investigations as well as for self-monitoring by general users.

Sign in / Sign up

Export Citation Format

Share Document