


Registros recuperados: 17  


DinizFilho,José Alexandre F.; Santos,Thiago; Rangel,Thiago Fernando; Bini,Luis Mauricio. 
Several metrics have been developed for estimating phylogenetic signal in comparative data. These may be important both in guiding future studies on correlated evolution and for inferring broadscale evolutionary and ecological processes (e.g., phylogenetic niche conservatism). Notwithstanding, the validity of some of these metrics is under debate, especially after the development of more sophisticated modelbased approaches that estimate departure from particular evolutionary models (i.e., Brownian motion). Here, two of these modelbased metrics (Blomberg's Kstatistics and Pagel's λ) are compared with three statistical approaches [Moran's I autocorrelation coefficient, coefficients of determination from the autoregressive method (ARM), and phylogenetic... 
Tipo: Info:eurepo/semantics/article 
Palavraschave: Autocorrelation; Blomberg's K; Pagel's lambda; Autoregressive method; Moran's I; Phylogenetic eigenvector regression. 
Ano: 2012 
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S141547572012000400019 
 

 

 

 

 

 


HurtadoTobón, L.H.; GarcíaGonzález, M.D.. 
The spatial autocorrelation range, understood here as the minimum distance between points at which the spatial correlation becomes zero, is a very useful parameter to design samples that have as goal the study of the spatial behavior of a variable. In this paper the estimation of the autocorrelation range by means of the correlograms of confidence bands are discussed in detail, and estimations for a set of physicalchemical variables studied at the Ciénaga Grande de Santa Marta, Northern Colombia, are made. The results of the application show that for four of the variables included in the study (Nitrite, Nitrate, the addition of Nitrite and Nitrate, and Oxygen concentration) it is not possible to analyze their spatial behavior because the... 
Tipo: Journal Contribution 
Palavraschave: Autocorrelation. 
Ano: 1999 
URL: http://hdl.handle.net/1834/3241 
 

 

 

 

 

 


Oliveira,Marcio Paulo de; Tavares,Maria Hermínia Ferreira; UribeOpazo,Miguel Angel; Timm,Luis Carlos. 
Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive statespace models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soilplantatmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial... 
Tipo: Info:eurepo/semantics/article 
Palavraschave: Autocorrelation; Cross correlation; Linear regression; Statespace model; Soil and plant properties. 
Ano: 2011 
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S010006832011000100009 
 

 

 


Pott,Cristiano A.; Jadoski,Sidnei O.; Schmalz,Britta; Hörmann,Georg; Fohrer,Nicola. 
Daily time series were used to verify the temporal variability and to characterize the nitrogen (N) and phosphorus (P) pollution in a 462 km² catchment of the Stör river, a typical rural lowland catchment in Germany. Also, this study aimed to identify the best sampling frequency of pollution by N and P. Total phosphorus (TP), soluble orthophosphatephosphorus (PO4P), particulatephosphorus (PP), total nitrogen (TN), nitratenitrogen (NO3N) ammoniumnitrogen (NH4N) and total suspended sediment (TSS) were analysed. Daily monitoring from August 8, 2009 until August 10, 2011 was conducted with an automatic water sampler at the outlet of the catchment. The results show a seasonal variability of water quality parameters with more N and P concentration in... 
Tipo: Info:eurepo/semantics/article 
Palavraschave: Water quality; Nitrate; Phosphate; Autocorrelation; Automatic water sampler; Seasonal variability. 
Ano: 2014 
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S141543662014000800005 
 


Chen, Dongling; Seale, James L., Jr.. 
We fit the Florida Model with an AR(1) error structure to pooled crosscountry International Comparison Project (ICP) data of Seale, Walker, and Kim and estimate the model with the minimum information (MI) estimator. Point estimates obtained by MI are similar in value to those obtained by Seale, Walker, and Kim with maximum likelihood (ML). Two similar simulations but with different sample sizes are conducted to compare the relative efficiencies of MI and ML with known and unknown (MLU) covariances. In the larger sample, the MLU is more efficient in terms of rootmeansquared errors (RMSEs) than the MI. Noteworthy, in the small sample, the MI is more efficient in terms of RMSEs than MLU, even though MLU explicitly accounts for AR(1), whereas the MI... 
Tipo: Journal Article 
Palavraschave: Autocorrelation; Crosscountry demand; Maximum likelihood; Minimum information; Pooled data. 
Ano: 2003 
URL: http://purl.umn.edu/43294 
 
Registros recuperados: 17  


