Improved Self Tuned Linear Predictor

2006 ◽  
Author(s):  
Novotny Wenceslao ◽  
O Perez Jorge ◽  
N Ferrao Hilda
Keyword(s):  
Linear Predictor

Frequency Estimation Based on Linear Predictor using Unbiased WLS Algorithm

2021 ◽  
Author(s):  
Prayuth Inban ◽  
Surapong Keanmanee ◽  
Rachu Punchalard ◽  
Wuthiporn Loedwassana
Keyword(s):  
Frequency Estimation ◽  
Linear Predictor

Main large data set features detection by a linear predictor model

2014 ◽  
Author(s):  
Carlos Enrique Gutierrez ◽  
Prof. Mohamad Reza Alsharif ◽  
Mahdi Khosravy ◽  
Prof. Katsumi Yamashita ◽  
Prof. Hayao Miyagi ◽  
...  
Keyword(s):  
Large Data ◽  
Data Set ◽  
Linear Predictor ◽  
Large Data Set ◽  
Predictor Model

scholarly journals Development and validation of the predictive risk of death model for adult patients admitted to intensive care units in Japan: an approach to improve the accuracy of healthcare quality measures

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Hideki Endo ◽  
Shigehiko Uchino ◽  
Satoru Hashimoto ◽  
Yoshitaka Aoki ◽  
Eiji Hashiba ◽  
...  
Keyword(s):  
Intensive Care ◽  
Quality Of Care ◽  
Generalized Linear Model ◽  
Healthcare Quality ◽  
Brier Score ◽  
Linear Predictor ◽  
Risk Of Death ◽  
Apache Iii ◽  
Funnel Plots

Abstract Background The Acute Physiology and Chronic Health Evaluation (APACHE) III-j model is widely used to predict mortality in Japanese intensive care units (ICUs). Although the model’s discrimination is excellent, its calibration is poor. APACHE III-j overestimates the risk of death, making its evaluation of healthcare quality inaccurate. This study aimed to improve the calibration of the model and develop a Japan Risk of Death (JROD) model for benchmarking purposes. Methods A retrospective analysis was conducted using a national clinical registry of ICU patients in Japan. Adult patients admitted to an ICU between April 1, 2018, and March 31, 2019, were included. The APACHE III-j model was recalibrated with the following models: Model 1, predicting mortality with an offset variable for the linear predictor of the APACHE III-j model using a generalized linear model; model 2, predicting mortality with the linear predictor of the APACHE III-j model using a generalized linear model; and model 3, predicting mortality with the linear predictor of the APACHE III-j model using a hierarchical generalized additive model. Model performance was assessed with the area under the receiver operating characteristic curve (AUROC), the Brier score, and the modified Hosmer–Lemeshow test. To confirm model applicability to evaluating quality of care, funnel plots of the standardized mortality ratio and exponentially weighted moving average (EWMA) charts for mortality were drawn. Results In total, 33,557 patients from 44 ICUs were included in the study population. ICU mortality was 3.8%, and hospital mortality was 8.1%. The AUROC, Brier score, and modified Hosmer–Lemeshow p value of the original model and models 1, 2, and 3 were 0.915, 0.062, and < .001; 0.915, 0.047, and < .001; 0.915, 0.047, and .002; and 0.917, 0.047, and .84, respectively. Except for model 3, the funnel plots showed overdispersion. The validity of the EWMA charts for the recalibrated models was determined by visual inspection. Conclusions Model 3 showed good performance and can be adopted as the JROD model for monitoring quality of care in an ICU, although further investigation of the clinical validity of outlier detection is required. This update method may also be useful in other settings.

scholarly journals Ecosystem physio-phenology revealed using circular statistics

2019 ◽  
Author(s):  
Daniel E. Pabon-Moreno ◽  
Talie Musavi ◽  
Mirco Migliavacca ◽  
Markus Reichstein ◽  
Christine Römermann ◽  
...  
Keyword(s):  
Climate Variability ◽  
Gross Primary Production ◽  
Ecosystem Dynamics ◽  
Circular Statistics ◽  
Individual Species ◽  
Vegetation Types ◽  
Linear Predictor ◽  
Growing Seasons ◽  
Circular Model ◽  
Circular Regression

Abstract. Quantifying responses of vegetation phenology to climate variability is a key prerequisite to predict shifts in how ecosystem dynamics due to climate change. So far, many studies have focused on responses of classical phenological events (e.g. budburst or flowering) to climatic variability for individual species. Comparatively little is known on physio-phenological events such as the timing of the maximum gross primary production (DOYGPPmax). However, understanding this type of physio-phenological phenomena is an essential element in predicting the response of the terrestrial carbon cycle to climate variability. In this study, we aim to understand how DOYGPPmax depends on climate drivers across 52 eddy-covariance (EC) sites in the FLUXNET network for different regions of the world. Most phenological studies rely on linear methods that cannot be generalized across both hemispheres and therefore do not allow for deriving general rules that can be applied for future predictions. Here we explore a new class of circular-linear (here called circular) regression approach that may show a path ahead. Circular regression allows relating circular variables (in our case phenological events) to linear predictor variables (e.g. climate conditions). As a proof of concept, we compare the performance of linear and circular regression to recover original coefficients of a predefined circular model on artificial and EC data. We then quantify the sensitivity of DOYGPPmax to air temperature, short-wave incoming radiation, precipitation and vapor pressure deficit using circular regressions. Finally, we evaluate the predictive power of the regression models for different vegetation types. Our results show that the DOYGPPmax of each FLUXNET site has a unique signature of climatic sensitivities. Overall radiation and temperature are the most relevant controlling factors of DOYGPPmax across sites. The circular approach gives us new insights at the site level. In a Mediterranean shrub-land, for instance, we find that the two growing seasons are controlled by different climatic factors. Although the sensitivity of the DOYGPPmax to the climate drivers is very site specific, it is possible to extrapolate the circular regression model across vegetation types. From a methodological point of view, our results reveal that circular regression is a robust alternative to conventional phenological analytic frameworks. In particular global analyses can benefit, where phase shifts play a role or double peaked growing seasons may occur.

scholarly journals Measuring the exposure of Black, Asian and other ethnic groups to Covid-infected neighbourhoods in English towns and cities.

2021 ◽  
Author(s):  
Richard Harris ◽  
Chris Brunsdon
Keyword(s):  
Ethnic Groups ◽  
Minority Groups ◽  
House Prices ◽  
Poisson Model ◽  
Linear Predictor ◽  
Random Intercept ◽  
Black Residents ◽  
Occupational And Environmental Exposure ◽  
Index Of Exposure ◽  
The Uk

Abstract Drawing on the work of The Doreen Lawrence Review – a report on the disproportionate impact of Covid-19 on Black, Asian and minority ethnic communities in the UK – this paper develops an index of exposure, measuring which ethnic groups have been most exposed to Covid-19 infected residential neighbourhoods during the first and second waves of the pandemic in England. The index is based on a Bayesian Poisson model with a random intercept in the linear predictor, allowing for extra-Poisson variation at neighbourhood and town/city scales. This permits within-city differences to be decoupled from broader regional trends in the disease. The research finds that members of ethnic minority groups tend to be living in areas with higher infection rates but also that the risk of exposure is distributed unevenly across these groups. Initially, in the first wave, the disease disproportionately affected Black residents. As the pandemic has progressed, especially the Pakistani but also the Bangladeshi and Indian groups have had the highest exposure. This higher exposure of the Pakistani group is not straightforwardly a function of neighbourhood deprivation because it is present across a range of average house prices. However, we find evidence to support the view, expressed in The Doreen Lawrence Review, that it is linked to occupational and environmental exposure, particularly residential density.

scholarly journals An Algorithm for Computing the Non-linear Predictor

1971 ◽  
Vol 42 (1) ◽  
pp. 75-80 ◽  
Author(s):  
James B. Robertson
Keyword(s):  
Linear Predictor ◽  
Non Linear

On dispersion of stable random vectors and its application in the prediction of multivariate stable processes

1994 ◽  
Vol 31 (3) ◽  
pp. 691-699 ◽  
Author(s):  
A. Reza Soltani ◽  
R. Moeanaddin
Keyword(s):  
Stable Process ◽  
Stable Processes ◽  
Random Vectors ◽  
Linear Predictor ◽  
Error Vector ◽  
Best Linear Predictor ◽  
Definition Of ◽  
Stable Random Vectors

Our aim in this article is to derive an expression for the best linear predictor of a multivariate symmetric α stable process based on many past values. For this purpose we introduce a definition of dispersion for symmetric α stable random vectors and choose the linear predictor which minimizes the dispersion of the error vector.

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