Covariance models. Medical search. Frequent questions

We propose to generalize the Wishart state-space model for realized covariance matrices of asset returns in order to capture complex measurement error structures induced by heterogeneous liquidity across assets. (ssrn.com)
package, which fits regression models to correlation/covariance matrices (which can be obtained from pairwise deletion), but the regression model does not appear to include an intercept parameter. (stackexchange.com)
Estimation of covariance matrices then deals with the question of how to approximate the actual covariance matrix on the basis of a sample from the multivariate distribution. (wikipedia.org)
Another issue is the robustness to outliers, to which sample covariance matrices are highly sensitive. (wikipedia.org)
Estimates of covariance matrices are required at the initial stages of principal component analysis and factor analysis, and are also involved in versions of regression analysis that treat the dependent variables in a data-set, jointly with the independent variable as the outcome of a random sample. (wikipedia.org)
If the cell covariance matrices are assumed to be equal, then there are some popular tests available to test the equality of the mean vectors. (hindawi.com)
When there are some departures from the standard assumption, that is, unequal cell covariance matrices, these solutions were proposed by James [ 2 ], Johansen [ 3 ], Gamage et al. (hindawi.com)
The more complex design is multifactor MANOVA especially when the homogeneity of the cell covariance matrices assumption is seriously violated. (hindawi.com)
Statistical inference for constants of proportionality between covariance matrices. (crossref.org)
Parsimonious group covariance functions can then defined by block covariance matrices with constant correlations between pairs of blocks and within blocks. (cam.ac.uk)
The same model can be used when the assumption within blocks is relaxed, giving a flexible parametric family of valid covariance matrices with constant correlations between pairs of blocks. (cam.ac.uk)
The problem of estimating covariance matrices in balanced multivariate variance components models is discussed. (edu.sa)
As with univariate models, it is possible for the traditional estimators, based on differences of the mean square matrices, to produce estimates that are outside the parameter space. (edu.sa)
In fact, in many cases it is extremely likely that traditional estimates of the covariance matrices will not be nonnegative definite (nnd). (edu.sa)
In this article we develop an iterative estimation procedure, satisfying a least squares criterion, that is guaranteed to produce nnd estimates of the covariance matrices, discuss the speed of convergence, and provide an example to show how the estimates change. (edu.sa)
Calvin, JA & Dykstra, RL 1991, ' Least squares estimation of covariance matrices in balanced multivariate variance components models ', Journal of the American Statistical Association , vol. 86, no. 414, pp. 388-395. (edu.sa)
Nonparametric estimation of large covariance matrices with conditional sparsity. (warwick.ac.uk)

Accurate estimation of latent heat flux (LE) based on remote sensing data is critical in characterizing terrestrial ecosystems and modeling land surface processes. (osti.gov)
The estimation of portfolio value-at-risk (VaR) requires a good estimate of the covariance matrix. (risk.net)
As it is well known that a sample covariance matrix based on some historical rolling window is noisy and is a poor estimate for the high-dimensional population covariance matrix, to estimate the conditional portfolio VaR we develop a framework using the dynamic conditional covariance model, within which various de-noising tools are employed for the estimation of the unconditional covariance target. (risk.net)
One approach to estimating the covariance matrix is to treat the estimation of each variance or pairwise covariance separately, and to use all the observations for which both variables have valid values. (wikipedia.org)
Version 3.0-0 of the R package 'sandwich' for robust covariance matrix estimation (HC, HAC, clustered, panel, and bootstrap) is now available from CRAN, accompanied by a new web page and a paper in the Journal of Statistical Software (JSS). (r-bloggers.com)
They are employed to adjust the inference following estimation of a standard least-squares regression or generalized linear model estimated by maximum likelihood. (r-bloggers.com)
property of the resulting model stores information about the estimation, such as handling of initial conditions and options used in estimation. (mathworks.com)
model by estimation, you can extract estimated coefficients and their uncertainties from the model. (mathworks.com)
model to configure an initial parameterization for estimation of a transfer function to fit measured response data. (mathworks.com)
Covariance estimation via sparse Kronecker structures. (warwick.ac.uk)

Twenty-two eddy covariance (EC) sites with LE observation were chosen to evaluate our algorithm, showing that the proposed EOF fusion method was capable of integrating the two satellite data sets with improved consistency and reduced uncertainties. (osti.gov)
Ferster, Colin J;Trofymow, JA (Tony);Coops, Nicholas C;Chen, Baozhang;Black, Thomas Andrew 2015-12-01 00:00:00 Background: The global network of eddy-covariance (EC) flux-towers has improved the understanding of the terrestrial carbon (C) cycle, however, the network has a relatively limited spatial extent compared to forest inventory data and plots. (sagepub.com)
These ΔC-based estimates were then compared with ΣNEP measured at an eddy-covariance flux-tower (EC-flux) and modelled by the Carbon Budget Model - Canadian Forest Sector (CBM-CFS3) using historic forest inventory and forest disturbance data. (sagepub.com)
This model was inverted by inputting eddy covariance CO2 and H2O fluxes from four black spruce forests in Alaska to infer spatially representative ecophysiological parameters using a global optimization technique. (akcasc.org)
A network of eddy covariance instrumentation installed across similar ecosystem types, such as the one used in this study, can be particularly useful for evaluating ecosystem-scale ecophysiological traits and their role under changing environmental conditions. (akcasc.org)
Here, we used the latest database of eddy covariance measurements to estimate θ crit across Europe by evaluating evaporative fraction (EF)-SM relationships and investigating the covariance between vapor pressure deficit (VPD) and gross primary production (GPP) during SM dry-down periods. (lu.se)

Our model assumes that the latent covariance matrix of the assets is observed through their realized covariance matrix with a Riesz measurement density, which generalizes the Wishart to monotone missing data. (ssrn.com)
This paper aims at providing an original Riemannian geometry to derive robust covariance matrix estimators in spiked models (i.e. when the covariance matrix has a low-rank plus identity structure). (ieee.org)
In statistics, sometimes the covariance matrix of a multivariate random variable is not known but has to be estimated. (wikipedia.org)
Simple cases, where observations are complete, can be dealt with by using the sample covariance matrix. (wikipedia.org)
In addition, if the random variable has a normal distribution, the sample covariance matrix has a Wishart distribution and a slightly differently scaled version of it is the maximum likelihood estimate. (wikipedia.org)
Statistical analyses of multivariate data often involve exploratory studies of the way in which the variables change in relation to one another and this may be followed up by explicit statistical models involving the covariance matrix of the variables. (wikipedia.org)
In the general case, the unbiased estimate of the covariance matrix provides an acceptable estimate when the data vectors in the observed data set are all complete: that is they contain no missing elements. (wikipedia.org)
Assuming the missing data are missing at random this results in an estimate for the covariance matrix which is unbiased. (wikipedia.org)
However, for many applications this estimate may not be acceptable because the estimated covariance matrix is not guaranteed to be positive semi-definite. (wikipedia.org)
This could lead to estimated correlations having absolute values which are greater than one, and/or a non-invertible covariance matrix. (wikipedia.org)
returns the parameter covariance as either a matrix or a structure, depending on the covariance type that is specified. (mathworks.com)
The covariance matrix entries corresponding to the delay parameter (fifth row and column) are zero because the delay was not estimated. (mathworks.com)
Get the covariance matrix for the estimated parameters. (mathworks.com)
covariance matrix, with entries corresponding to the four estimated parameters. (mathworks.com)
The sandwich package provides model-robust covariance matrix estimators for cross-sectional, time series, clustered, panel, and longitudinal data. (r-bloggers.com)
Then, we give a parameterization of the block covariance matrix described above, based on a hierarchical Gaussian model. (cam.ac.uk)
In factor analysis and structural equation modeling non-normal data simulation is traditionally performed by specifying univariate skewness and kurtosis together with the target covariance matrix. (bi.no)
We present PLSIM, a new method for generating nonnormal data with a pre-specified covariance matrix that is based on coordinate-wise piecewise linear transformations of standard normal variables. (bi.no)
This sum of KP is estimated such that it approximates the spatiotemporal sample covariance best in matrix norm. (vu.nl)
Taking enough terms into account, the spatiotemporal sample covariance matrix can be described exactly by this extended model. (vu.nl)
Furthermore, temporal nonstationarities due to trial-to-trial variations are not clearly visible in the first two terms, and, hence, play only a minor role in the sample covariance matrix in terms of matrix power. (vu.nl)
Heethaar, R.M. / The spatiotemporal MEG covariance matrix modeled as a sum of Kronecker products . (vu.nl)
Rovine, MJ & Molenaar, PCM 1998, ' A LISREL model for the analysis of repeated measures with a patterned covariance matrix ', Structural Equation Modeling , vol. 5, no. 4, pp. 318-343. (psu.edu)
R Code and an Example for sparse matrix graphical models in Leng and Tang (JASA, 2012). (warwick.ac.uk)

We propose a new framework for modeling and forecasting common financial risks based on (un)reliable realized covariance measures constructed from high-frequency intraday data. (duke.edu)
Our new approach explicitly incorporates the effect of measurement errors and time-varying attenuation biases into the covariance forecasts, by allowing the ex-ante predictions to respond more (less) aggressively to changes in the ex-post realized covariance measures when they are more (less) reliable. (duke.edu)

An appropriate covariance structure is selected, tested, and compared with alternative patterns. (psu.edu)
Using a mixed-model methodology, the appropriate covariance structure was identified and utilized to model the measurements. (cdc.gov)

Developing methods to use inventory-based and EC flux measurements together with modeling approaches is necessary evaluate forest C dynamics across broad spatial extents. (sagepub.com)
Measurements are typ- therefore critical to understand forest C dynamics, includ- ically made of individual plants, for example, all trees ing forest C stock components and transfer mechanisms within a plot are measured for diameter, species, and in order to develop accurate forest C models such as the height, then allometric relationships are used to deter- Carbon Budget Model of the Canadian Forest Sector mine tree mass (Ter-Mikaelian 1997). (sagepub.com)
A model to predict 24-h urinary creatinine using repeated measurements in an occupational cohort study. (cdc.gov)
In conclusion, the use of a covariance structure that allowed repeated measurements for any one individual to be correlated, improved the predictive performance. (cdc.gov)

testing compound hypotheses and the application of the regression model to the analyses of variance and covariance, and -structural equation models and influence statistics. (springer.com)
Analysis of variance and covariance of postintervention use and intention to use HPDs and a meta-analytic test were done. (cdc.gov)
Assuming continuity of the modeling function the variance is given in terms of conditional probabilities extracted from the data. (lu.se)
3) k=1 where denotes the y-variable variance and the angled brackets covariances. (lu.se)
However, this robustness comes with the downside that the non-parametric methods have, in general, larger variance in In this work, we extend the popular sparse iterative covariance- the estimates compared to the parametric approaches, which based estimator (SPICE) by generalizing the formulation to typically in turn are less robust [5]. (lu.se)

Recently available mixed model methodology permits the covariance structure to be incorporated into the statistical model. (duke.edu)
Specifically, this study shows how a statistical method which is covariance analysis can be used to solve an inadequacy of experimental design. (scialert.net)
Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R." Journal of Statistical Software , 95 (1), 1-36. (r-bloggers.com)
Stroup believes that early awareness of the full GLMM picture will help the working statistician improve his/her ability to understand issues in experimental design and statistical modeling, even in those cases where GLMM in its fullness is not required. (maa.org)
Accordingly the book begins with ground-up treatments of the concept of a statistical model (Chapter One, Modeling Basics) and the design of experiments and studies (Chapter Two, Design Matters). (maa.org)
Translating these statistical improvements into economic gains, we find that under empirically realistic assumptions a risk-averse investor would be willing to pay up to 170 basis points per year to shift to using the new class of forecasting models. (duke.edu)
Discussion of 'Statistical modelling of citation exchange between statistics journals' by Varin, Cattelan and Firth, Journal of the Royal Statistical Society Series A, 179, 54. (warwick.ac.uk)
Published statistical models exist to predict 24-h creatinine, but many were developed assuming independence among observations. (cdc.gov)
independently suggest and analyze stochastic models for high-dimensional data, in particular in spatial statistics and image analysis, · independently implement a computer program for the solution of a given statistical problem and relating analysis method, · present motivations, course of action, and conclusions in the solution of a given statistical problem, both written and orally. (lu.se)

The MIXED procedure of the SAS((R)) System provides a rich selection of covariance structures through the RANDOM and REPEATED statements. (duke.edu)
Various continuously-indexed spatio-temporal process models have been constructed to characterize spatio-temporal dependence structures, but the computational complexity for model fitting and predictions grows in a cubic order with the size of dataset and application of such models is not feasible for large datasets. (tamu.edu)

is the order of the autoregressive (AR) model used to produce the PSD estimate. (mathworks.com)
Estimate the PSD using the covariance method. (mathworks.com)
Estimate the PSD of the signal using the covariance method with a 12th-order autoregressive model. (mathworks.com)
Estimate the model. (mathworks.com)

Ignoring covariance structure may result in erroneous inference, and avoiding it may result in inefficient inference. (duke.edu)
However, once the covariance structure is modelled, inference about fixed effects proceeds essentially as when using PROC GLM. (duke.edu)
Fallacy of data-selective inference in modelling networks. (warwick.ac.uk)
identify and problematize possibilities and limitations of stochastic modelling and inference, in particular in high-dimensional problems, · be able to assume a stochastic point of view on random variation in natural phenomena. (lu.se)

We validate the model empirically by using various coverage tests and loss function measures and discover that the choice of de-noising treatments for the covariance target plays a critical role in measuring the accuracy of the dynamic portfolio VaR estimates. (risk.net)
The example also illustrates the effects of choice of covariance structure on tests and estimates of fixed effects. (duke.edu)
In many situations, estimates of linear combinations are invariant with respect to covariance structure, yet standard errors of the estimates may still depend on the covariance structure. (duke.edu)
to provide sample estimates that can be used for model checking. (wikipedia.org)
The reason for the factor n − 1 rather than n is essentially the same as the reason for the same factor appearing in unbiased estimates of sample variances and sample covariances, which relates to the fact that the mean is not known and is replaced by the sample mean (see Bessel's correction). (wikipedia.org)
Footprint analysis was used with remote sensing, soils and topography data to evaluate how well the inventory plots represented the range of stand conditions within the area of the flux-tower footprint and to spatially scale the plot data to the area of the EC-flux and model based estimates. (sagepub.com)

Based on the model with optimized parameters, small but clear increases in gross primary productivity (GPP) and decreases in latent heat flux (LE) were estimated to be associated with rising [CO2] from 2002 to 2014 (p (akcasc.org)

Another was to avoid the covariance structure issue by analysing transformed data or making adjustments to otherwise inadequate analyses. (duke.edu)
Given that lifestyle factors do not occur in isolation, multivariable analyses provide a more realistic model of the lifestyle - brain relationship. (bvsalud.org)

Bayesian methods for stochastic modelling, classification and reconstruction. (lu.se)
The resulting covariance pattern was further explored with Bayesian regressions. (bvsalud.org)

Scholars@Duke publication: Modelling covariance structure in the analysis of repeated measures data. (duke.edu)

Modelling the covariance structure is a major hurdle in the use of PROC MIXED. (duke.edu)
Similarities between the LISREL model and SAS PROC MIXED are discussed. (psu.edu)

Additional studies probe the extent to which violation of the standard assumption of underlying normality leads to bias in correlations and factor models. (bi.no)

The results suggest that climate models simulate a cloud response to a change in sea ice that is stronger than observations and that the offset of the surface albedo feedback by an increase in Arctic cloudiness is likely too strong. (confex.com)
The SCDA not only uses the coupled model to generate the forecast and assimilate observations into multiple model components like the weakly coupled version (WCDA), but also applies a cross update using the coupled covariance between variables from different model components. (ametsoc.org)
The LACC method utilizes such asymmetric structure by using the leading forecasts and observations of the fast atmospheric variable for cross update, therefore, increasing the coupled correlation and enhancing the signal-to-noise ratio in calculating the coupled covariance. (ametsoc.org)

Characterising the covariance pattern between lifestyle factors and structural brain measures: a multivariable replication study of two independent ageing cohorts. (bvsalud.org)

For any choice of norms, oping so-called semi-parametric approaches, which typically the resulting generalized SPICE method enjoys the same make some model structure assumptions, although restrain benefits as the regular SPICE method, including being hyper- from making strong model order assumptions, other than as- parameter free, although the choice of norm is shown to suming that the solution is sparse. (lu.se)
Index Terms-- Covariance fitting, sparse reconstruction, aid in the choice of this parameter, whereas, in other, some convex optimization kind of rule of thumb on how to choose it may be found [12]. (lu.se)

A suite of code for covariance modelling in longitudinal data, including an implementation of the method in Zhang, Leng, and Tang (JRSSB, 2015), can be found here . (warwick.ac.uk)
Discrete longitudinal data modeling with a mean-correlation regression approach. (warwick.ac.uk)

An example from the pharmaceutical industry is used to illustrate how to choose a covariance structure. (duke.edu)
We illustrate how to simulate continuous and ordinal data for covariance modeling, and how to use the new package discnorm to test for underlying normality in ordinal data. (bi.no)

Order of the autoregressive model, specified as a positive integer. (mathworks.com)

Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions " 207, no. 1 (2018): 71-91. (duke.edu)

Associations of MSSS and the MetSyn with CRAE/CRVE were obtained using linear regression models or Analysis of Covariance. (cdc.gov)

In this investigation, a large number of covariance datasets were employed for the BP neural network training. (phys.org)

Firstly, the author introduced the collision analysis model. (phys.org)
In the collision analysis, orbit prediction and covariance prediction were conducted according to the initial states and the initial covariances of two space objects. (phys.org)
The cross update could potentially improve the balance and quality of the analysis, but its implementation has remained a great challenge in practice because of different time scales between model components. (ametsoc.org)
With the LACC method, the SCDA reduces the analysis error of the oceanic variable by over 20% compared to the WCDA and 10% compared to the SCDA using simultaneous coupled covariance. (ametsoc.org)
Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. (bvsalud.org)
2/ 4 · identify and describe stochastic models and analysis methods for high- dimensional problems, in particular regarding spatial statistics and image analysis. (lu.se)

The statespace approach allows for a flexible description of the covariance dynamics implied by the data and an empirical application shows that the model performs very well in- and out-of-sample. (ssrn.com)
However, according to our simulation studies, the empirical sizes of the AHT test for the two-factor nested MANOVA model may far exceed the nominal level. (hindawi.com)
In an empirical atmospheric density model, parameters such as solar activity and geomagnetic activity were employed to describe the atmospheric status. (phys.org)
At present, commonly used empirical atmospheric density models were the family of Jacchia models and MSISE models. (phys.org)
The second step was to applied the dynamic inversion method to optimize parameters in the selected empirical atmosphere density models. (phys.org)

A novel perspective of sea ice-cloud interactions in the Arctic is provided here through a satellite footprint-level quantification of the covariance between sea ice and Arctic low cloud properties from NASA A-Train active remote sensing satellite data. (confex.com)
I'm trying to fit a multiple regression model with pairwise deletion in the context of missing data. (stackexchange.com)
returns the covariance data of only the free model parameters. (mathworks.com)
models because these models do not store parameter covariance data. (mathworks.com)
Clustered covariances or clustered standard errors are very widely used to account for correlated or clustered data, especially in economics, political sciences, and other social sciences. (r-bloggers.com)
In order to test the performances of the atmospheric density model correction method, a total of five-day orbit data of Q-sat from January 11 to January 15, 2022 were selected. (phys.org)
We demonstrate this model for a 5 x 2 (Time x Group) ANOVA in which the data are assumed to be serially correlated. (psu.edu)
Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). (ametsoc.org)
This paper studies a new leading averaged coupled covariance (LACC) method for the strongly coupled data assimilation (SCDA). (ametsoc.org)
Using correlated repeated measurement data collected from an occupational cohort, the objectives were to create a predictive model for 24-h urinary creatinine and to compare the predictive capability of this model to earlier published models. (cdc.gov)
A backwards elimination model building technique applied to the model building data set (110 adult male subjects and 457 creatinine values) yielded a final model that included variables for body mass index (BMI), height, diabetes, allergies, medical conditions that affect kidney function, use of creatine supplements, and anti-inflammatory medications. (cdc.gov)
Factor loadings, t-values and fit indices showed that the SCT model was fitted to the data. (who.int)
One fits the data to a model, r a particular choice of F , and then interprets the deviation of the fit as noise. (lu.se)
Hence, all probabilistic parame- its for hazardous chemicals is protection of human ters used in the model are based on actual human data. (cdc.gov)

The advantage of the LACC method is more notable when the system contains larger errors, such as in the cases with smaller ensemble size, bigger time-scale difference, or model biases. (ametsoc.org)

The single Kronecker product (KP) model for the spatiotemporal covariance of MEG residuals is extended to a sum of Kronecker products. (vu.nl)

The algorithm is based on an incremental clustering framework and leads to a topological model where the images of each zone tend to be clustered in different nodes. (mdpi.com)

However, considerable uncertainties (greater than 100%) were estimated in the magnitude of the fertilization effect associated with different parameterizations in the biochemical model, indicating the need for ecophysiological studies for boreal plants. (akcasc.org)

Comparing the predicted residual errors with those de- rived from a linear model assumption provides a signal for nonlinearity. (lu.se)

R Code (rar file) for variable selection in heteroscedastic linear models. (warwick.ac.uk)

Is the Subject Area "Simulation and modeling" applicable to this article? (plos.org)

One choice was to ignore covariance structure and make invalid assumptions. (duke.edu)
To address whether excessive, prolonged, and voluntary alcohol consumption per se can disturb brain structure, animal models are essential. (nature.com)

A statistically significant covariance between cloud fraction, cloud liquid water, cloud ice water, cloud total water and their vertical distributions is found with sea ice concentration in each atmospheric regime and season. (confex.com)

returns the raw covariance of the parameters of an identified model. (mathworks.com)
model with six parameters, four of which are estimated. (mathworks.com)
model, has six parameters, four of which are estimated. (mathworks.com)
Get the factored covariance for the estimated parameters. (mathworks.com)
The first step was to choose the atmospheric density model and its correction parameters. (phys.org)
For selection of atmospheric density model to be optimized, it was necessary to consider the sensitivity of the parameters in the model, and the performance of the model in terms of orbit prediction. (phys.org)
models, the polynomial coefficients and transport delays of each input-output pair are independently estimable parameters. (mathworks.com)
creates a continuous-time transfer function model with identifiable parameters. (mathworks.com)

Get the raw parameter covariance for the model. (mathworks.com)
Get the raw parameter covariance for the models in the array. (mathworks.com)

Structural Equation Modeling, s. 1- 12. (bi.no)
The present article aims to introduce structural equation modeling, in particular measured variable path models, and discuss their great potential for corpus linguists. (degruyter.com)
Structural Equation Modeling , 5 (4), 318-343. (psu.edu)

In this work, an incremental clustering approach to obtain compact hierarchical models of an environment is developed and evaluated. (mdpi.com)

Conventional procedures for estimating 2 are model-based. (lu.se)

Furthermore, while the linear regression model is certainly the most important application case, the same strategies can be employed in more general models (e.g., for zero-inflated, censored, or limited responses). (r-bloggers.com)
In R, functions for covariances in clustered or panel models have been somewhat scattered or available only for certain modeling functions, notably the (generalized) linear regression model. (r-bloggers.com)
Walter Stroup is a leading authority on generalized linear mixed models (GLMMs) for applied statisticians, especially as implemented in the SAS programming environment. (maa.org)
He offers here a thorough, engaging and opinionated treatment of the subject, one that he says is directed to "graduate students in statistics, statistics professionals seeking to get up to speed, and researchers new to the generalized linear model thought process. (maa.org)
My own evaluation is based on my status as someone in between a "statistics professional trying to get up to speed" and a "researcher new to the generalized linear model thought process. (maa.org)
In this letter we devise a method for estimating the optimum r when the modeling function F is not restricted to be linear. (lu.se)
Comparing the obtained 2 with what is extracted assuming a linear model in Eq. (3) r provides means for establishing nonlinearities. (lu.se)

Received May 14, 1997 variability and to use this measure of variability to Using the Monte Carlo method and physiologically derive a safe exposure limit or guidance value for inha- based pharmacokinetic modeling, an occupational in- lation of trichloroethylene (TRI) in an occupational set- halation exposure to trichloroethylene consisting of ting. (cdc.gov)

Whereas the single KP model can be interpreted by assuming that background activity is generated by randomly distributed dipoles with certain spatial and temporal characteristics, the sum model can be physiologically interpreted by assuming a composite of such processes. (vu.nl)
To quantify the magnitude of this fertilization effect, we have developed a big-leaf model that couples photosynthesis and stomatal conductance processes. (akcasc.org)

For examples, see Create Continuous-Time Transfer Function Model and Create Discrete-Time Transfer Function . (mathworks.com)

Although many publications just refer to "the'' clustered standard errors, there is a surprisingly wide variety of clustered covariances, particularly due to different flavors of bias corrections. (r-bloggers.com)

In addition to increased methodological versatility, this technique encourages big-picture, model-based reasoning, thus allowing corpus linguists to move away from the, at times, somewhat overly simplified mindset brought about by the more narrow null-hypothesis significance testing paradigm. (degruyter.com)

Anatomy4

Organisms2

Diseases5

Chemicals and Drugs2

Analytical, Diagnostic and Therapeutic Techniques and Equipment46

Psychiatry and Psychology6

Phenomena and Processes31

Disciplines and Occupations5

Anthropology, Education, Sociology and Social Phenomena1

Humanities1

Information Science9

Named Groups3

Health Care30

Matrices17

Estimation10

Eddy6

Matrix24

Realized Covariance Measures2

Appropriate covariance structure2

Measurements4

Variance5

Statistical9

Structures2

Estimate4

Inference4

Estimates6

Latent1

Analyses2

Bayesian2

Scholars@Duke1

PROC MIXED2

Correlations1

Observations3

Measures1

Sparse2

Longitudinal data2

Illustrate2

Autoregressive1

20181

Regression models1

Datasets1

Analysis6

Empirical5

Data14

Biases1

Spatiotemporal1

Algorithm1

Boreal1

Assumption1

Heteroscedastic1

Simulation1

Structure2

Distributions1

Parameters8

Parameter2

Structural Equation3

Hierarchical1

Conventional1

Linear7

Derive1

Processes2

Examples1

Bias1

Hypothesis1