[PDF] Top 20 A New Nonparametric Regression for Longitudinal Data
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A New Nonparametric Regression for Longitudinal Data
... of regression methods many researchers are wishing to analyze their data by using regression methods, but the fundamental assumption in the regression methods is normality distribution of ... See full document
13
Discrete longitudinal data modeling with a mean correlation regression approach
... liver data set as in Appendix D of Fleming and Harrington (1991). The PBC data set was collected in a study conducted by the Mayo Clinic from 1974 to 1984 and is available in many R packages ...a new ... See full document
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Uniform convergence of estimator for nonparametric regression with dependent data
... 16. Bosq, D, Blanke, D: Inference and Prediction in Large Dimensions. Wiley, Chichester (2007) 17. Fan, JQ: Design-adaptive nonparametric regression. J. Am. Stat. Assoc. 87, 998-1004 (1992) 18. Fan, JQ: ... See full document
12
A semiparametric regression model for longitudinal data with non stationary errors
... a new semiparametric longitudinal mean-covariance model in which the effects on dependent variable of some explanatory variables are linear and others are nonlinear, while the within-subject correlations are ... See full document
29
Modified Cp Criterion for Optimizing Ridge and Smooth Parameters in the MGR Estimator for the Nonparametric GMANOVA Model
... in nonparametric regression, an overfitting problem ...linear regression model, when the number of covariates be- comes large, the estimator of the varying coefficient becomes ...spline ... See full document
14
Nonparametric and semiparametric regression model selection
... in nonparametric and semiparametric ...a new design point with the same convenience as in linear ...fully nonparametric time series model for a given set of data, our research suggests using ... See full document
39
Estimation of Nonparametric Regression Models with Measurement Error Using Validation Data
... We consider the problem of estimating a function g in nonparametric regres- sion model when only some of covariates are measured with errors with the assistance of validation data. Without specifying any ... See full document
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A new data analysis approach for measuring longitudinal changes of metabolism in cognitively normal elderly adults
... A linear regression model (R software package, www.r- project.org) was used to determine whether the presence of APOE-E4 was associated with a faster rFTC decline. The rFTC decline was characterized by 1) ... See full document
8
Bayesian Nonparametric Covariance Regression
... Trends data set (available at ...such data are Markov random fields ...between New York and ...exploratory data analysis, we find that the flu data does not yield a sparse graphical ... See full document
42
Nonparametric Rotations for Sphere-Sphere Regression
... of new landmarks, and leads to a better understanding of the distribution of species and ecosystems in space and ...experimental data from vectorcardiograms, which capture aspects of the electrocardiogram ... See full document
24
Estimation of functional sparsity in nonparametric varying coefficient models for longitudinal data analysis
... ChIP-chip data contains the binding information of 106 transcription factors, among which 21 TFs are confirmed to be related to cell cycle regulation by ...squares regression to study which TFs are ... See full document
29
The performance of nonparametric regression for trend and seasonal pattern in longitudinal data
... The performance indicators are associated with goodness of fit in the selected estimator based on the quantity measured for smoothing. In this case, we use three kinds of performance indicator commonly used in ... See full document
5
Modelling Local Polynomial for longitudinal data a case study: Inflation sectors in Indonesia
... of nonparametric regression methods that have been developed besides spline and kernel is Local ...nonparamteric regression with local polynomial in autocorrelation error (Xiao et ...polynomial ... See full document
6
Longitudinal Survey, Nonmonotone, Nonresponse, Imputation, Nonparametric Regression
... the longitudinal survey data which often has nonignorable ...linear regression is used to impute the missing values and then the estimation of the time-dependent finite populations ...and ... See full document
17
Optimal Estimation of Derivatives in Nonparametric Regression
... Fan and Gijbels, 1995; Zhou and Wolfe, 2000; Boente and Rodriguez, 2006; Cao, 2014). We note, however, that the optimal bandwidths may differ for estimating the regression function and for estimating the ... See full document
25
Nonparametric Regression Estimation with Mixed Measurement Errors
... the new esti- mators are promising in terms of correcting the bias arising from the errors-in- ...of nonparametric errors-in- variables regression (see, ... See full document
17
Estimating Lyapunov exponents with nonparametric regression
... For a sample size in the range of 2000-2500 and no measurement error we evaluated the performance of the four approximation methods described in Section 4 ( Local Spline, Radial Basis, N[r] ... See full document
33
Endogeneity in nonparametric and semiparametric regression models
... Wklv sdshu frqvlghuv wkh qrqsdudphwulf dqg vhplsdudphwulf phwkrgv iru hvwlpdwlqj uhjuhvvlrq prghov zlwk frqwlqxrxv hqgrjhqrxv uhjuhvvruv1 Zh olvw d qxpehu ri glhuhqw jhqhudol}dwlrqv ri w[r] ... See full document
72
New Nonparametric Rank Based Tests for Paired Data
... used nonparametric test for paired data when at least one of the assumptions is not ...a nonparametric statistical test for assessing whether the two independent samples are from the same ...paired ... See full document
10
Change Point Detection for General Nonparametric Regression Models
... general nonparametric regres- sion model under mild conditions. New proofs are given to prove the weak convergence of the underlying processes which assume remove the stringent condition of bounded total ... See full document
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