Residual Analysis in Rasch Counts Models

2021 ◽  
pp. 285-295
Author(s):  
Naiara Caroline Aparecido dos Santos ◽  
Jorge Luis Bazán
Keyword(s):  
Residual Analysis

Residual analysis in random regressions using SAS and S-PLUS

1999 ◽  
Vol 58 (3) ◽  
pp. 281-282 ◽  
Author(s):  
Sati Mazumdar ◽  
Amy E. Begley ◽  
Patricia R. Houck ◽  
Ying Yang ◽  
Charles F. Reynolds ◽  
...  
Keyword(s):  
Residual Analysis

scholarly journals Can the heterogeneity in stream dissolved organic carbon be explained by contributing landscape elements?

2014 ◽  
Vol 11 (4) ◽  
pp. 1199-1213 ◽  
Author(s):  
A. M. Ågren ◽  
I. Buffam ◽  
D. M. Cooper ◽  
T. Tiwari ◽  
C. D. Evans ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Dissolved Organic Carbon ◽  
Model Development ◽  
Field Measurements ◽  
Mixing Model ◽  
Residual Analysis ◽  
Residence Times ◽  
Stream Network ◽  
Landscape Elements ◽  
Modelling Studies

Abstract. The controls on stream dissolved organic carbon (DOC) concentrations were investigated in a 68 km2 catchment by applying a landscape-mixing model to test if downstream concentrations could be predicted from contributing landscape elements. The landscape-mixing model reproduced the DOC concentration well throughout the stream network during times of high and intermediate discharge. The landscape-mixing model approach is conceptually simple and easy to apply, requiring relatively few field measurements and minimal parameterisation. Our interpretation is that the higher degree of hydrological connectivity during high flows, combined with shorter stream residence times, increased the predictive power of this whole watershed-based mixing model. The model was also useful for providing a baseline for residual analysis, which highlighted areas for further conceptual model development. The residual analysis indicated areas of the stream network that were not well represented by simple mixing of headwaters, as well as flow conditions during which simple mixing based on headwater watershed characteristics did not apply. Specifically, we found that during periods of baseflow the larger valley streams had much lower DOC concentrations than would be predicted by simple mixing. Longer stream residence times during baseflow and changing hydrological flow paths were suggested as potential reasons for this pattern. This study highlights how a simple landscape-mixing model can be used for predictions as well as providing a baseline for residual analysis, which suggest potential mechanisms to be further explored using more focused field and process-based modelling studies.

scholarly journals Chemical residuality in maize (Zea mays L.) fields irrigated with deep well water

2018 ◽  
Vol 5 (13) ◽  
pp. 111
Author(s):  
Ma. Elizabeth Azpilcueta-Pérez ◽  
Aurelio Pedroza Sandoval ◽  
Ricardo Trejo-Calzada ◽  
Ignacio Sanchez-Cohen ◽  
María Del Rosario Jacobo-Salcedo
Keyword(s):  
Heavy Metals ◽  
Zea Mays ◽  
Zea Mays L ◽  
Residual Analysis ◽  
Maize Plant ◽  
Well Water ◽  
Deep Well ◽  
Fodder Production ◽  
Heavy Metals And Arsenic

The aim was to conduct a residual analysis of the main cationic elements, heavy metals and arsenic in irrigated maize fodder production. Four soil and maize plant samplings were conducted in eight sites in April, May, June and July, 2014. Ca, Na, As, and Pb concentrations were higher in the soil. The As concentration was higher in June and July. La Purísima had a higher As concentration, while Bermejillo, La Galicia and La Rosita had a higher Ca concentration. K, Ca, Pb and Zn had higher concentrations in the maize plant, with Ca, Na and K having highervalues in July and Mg, Pb and Zn being higher in May and July. The content of Ca, Mg, Na and K did not dier among regions; arsenic was higher in Leon Guzmán and La Rosita.

scholarly journals Radar: Residual Analysis for Anomaly Detection in Attributed Networks

2017 ◽  
Author(s):  
Jundong Li ◽  
Harsh Dani ◽  
Xia Hu ◽  
Huan Liu
Keyword(s):  
Anomaly Detection ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Residual Analysis ◽  
Network Information ◽  
Learning Framework ◽  
Modeling Process ◽  
Data Representations ◽  
Gene Regulatory ◽  
Attributed Networks

Attributed networks are pervasive in different domains, ranging from social networks, gene regulatory networks to financial transaction networks. This kind of rich network representation presents challenges for anomaly detection due to the heterogeneity of two data representations. A vast majority of existing algorithms assume certain properties of anomalies are given a prior. Since various types of anomalies in real-world attributed networks co-exist, the assumption that priori knowledge regarding anomalies is available does not hold. In this paper, we investigate the problem of anomaly detection in attributed networks generally from a residual analysis perspective, which has been shown to be effective in traditional anomaly detection problems. However, it is a non-trivial task in attributed networks as interactions among instances complicate the residual modeling process. Methodologically, we propose a learning framework to characterize the residuals of attribute information and its coherence with network information for anomaly detection. By learning and analyzing the residuals, we detect anomalies whose behaviors are singularly different from the majority. Experiments on real datasets show the effectiveness and generality of the proposed framework.

Parameter Estimation and Residual Analysis A Comparison

1994 ◽  
Vol 27 (5) ◽  
pp. 701-705
Author(s):  
Christian Schneider ◽  
Dieter Filbert
Keyword(s):  
Parameter Estimation ◽  
Residual Analysis
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