search for: semiparametr

Dear List's Members, I'm trying to implement "1. Roger Klein and Francis Vella, ?A semiparametric model for binary response and continuous outcomes under index heteroscedasticity,? Journal of Applied Econometrics 24, no. 5 (2009): 735-762. " estimator. I have a technical doubt about the choice of the optimizer for the likelihood function maximization. That of pg. 743, the function is Q....

>Date: Fri, 22 Feb 2002 09:48:16 -0600 (CST) >From: sun at cae.wisc.edu >Subject: [R] Does R have semiparametric package for time series > >Hello, All: I am new to R. I wonder if there is package for nonparametric or >semiparametric processing of time series. Thank you very much. > >Hongyu Sun Have a look at package "sm" and, in particular function "sm.autoregression". b...

Dear R-Help, I am Deniz. I am currently trying to replicate a semiparametric sample selection paper and I am working on Klein and Spady estimator. I am using the npindex() and npindexbw() functions. The problem is, I need results for single bandwidth and when I set bandwidth computation to "FALSE" mode, R is not optimizing anything. Here is the code I am using:...

folks, does anyone know if the SEMIFAR model has been implemented in R? i see that there's a S-FinMetrics function SEMIFAR() that does the job, but I have no access to that software. essentially, this semiparametric fractional autoregressive model introduces a deterministic trend to the FARIMA(p,d,0) model (which, as i understand it, takes care of the random trend and short and long memory). if not, are there any suggestions for how to estimate the model: phi(L) (1 - L)^d [y(t)(1 - L) - g(t/T)] = epsilon...

Dear List, I am pleased to announce the release of version 1.0.0 of DPpackage on CRAN. DPpackage covers some important models using Dirichlet process priors. The package includes: Semiparametric Bernoulli regression Semiparametric Density estimation Semiparametric Linear mixed models Semiparametric Generalized linear mixed models Semiparametric AFT model for interval-censored data I would very much appreciate any comments/errors/suggestions for future development. Best regards, Al...

Dear Professor, I am preparing for a Ph.D in semiparametric regression at Cairo university in Egypt. Referring to R package KernGPLM, I obtained R version 2.4.1 but I did not find package KernGPLM. Please, help me how can I obtain this package. Thanks in advance. Name: Magda Haggag E-mail: magdahaggag@yahoo.com Address: 27, Notrdam Desion...

Get some question about the function "gam". Suppose I have a semiparametric model, Y~x1+x2+s(z1). Using "gam", how could I get the estimates for the parametric part and nonparametric part respectively? And another question: we could find the coefficients for both parametric term and nonparametric term, what do these coefficients for the nonparametric term st...

Dear Users, I am new to both of things, so do not blame me too much... I am busy with semiparametric regression and use WinBUGS to sample posteriors. The code to call Winbugs is as follows: data <- list("y","X","n","m") #My variables inits.beta <- rep(0,K) inits.beta0 <- 0 inits <- function(){list(beta=inits.beta,beta0=inits.beta0,taueps=1...

...mooth is quite a long way from the data. When you include the intercept (m1) then the intercept is effectively shifting the constrained curve up towards the data, and you get a nice fit. Why? I haven't read Simon's book in great detail, though I have read Ruppert et al.'s Semiparametric Regression. I don't see a reason why a penalized spline model shouldn't equal the intercept (or zero) when all of the regressors equals zero. Is anyone able to help with a bit of intuition? Or relevant passages from a good description of why this would be the case? Furthermore, why...

1) I have a semiparametric model, like *Y~x1+s(x2)+s(x3)* When I rum gam package I only obtained the estimates and the statistics of the nonparametric part. How can I get the parametric part? Please could you give me the complete comand to do it. 2) How are the negative coefficients identified. I run different examples an...

...X Model 2: y2 ~ X + s(x1, x2) Resid. Df Resid. Dev Df Deviance F Pr(>F) 1 3819.5350 33921 2 3821.8323 33967 -2.2973 -46 2.2391 0.09863 . An F statistic is reported for comparing the linear model with the additively separable semiparametric model, and for comparing the additively separable model with the non-additvely separable model, but not when comparing the partially quadratic model (x#sq means x#^2) with the additively separable semiparametric model. I'm happy to provide more information about my dataset or my estimation,...

I am using semiparametric Model  library(mgcv) sm1=gam(y~x1+s(x2),family=binomial, f) How should I  find out standard error for ed50 for the above model ED50 =( -sm1$coef[1]-f(x2)) / sm1$coef [2]   f(x2) is estimated value for non parametric term.   Thanks [[alternative HTML version deleted]]

Hello, My question concerns model selection, stepAIC(), add1(), and coxph(). In Venables and Ripley (3rd Ed) pp389-390 there is an example of using stepAIC() for the automated selection of a coxph model for VA lung cancer data. A statistics question: Can partial likelihoods be interpreted in the same manner as likelihoods with respect to information based criterion and likelihood ratio tests?

...new package titled `np' is now available from CRAN. The package implements recently developed kernel methods that seamlessly handle the mix of continuous, unordered, and ordered factor datatypes often found in applied settings. The package also allows users to create their own nonparametric/semiparametric routines using high-level function calls (via the function npksum()) rather than writing their own C or Fortran code. Much of the code underlying the package is written in C including the function npksum(). Currently, a range of methods can be found in the package including - multivariate nonpa...

...new package titled `np' is now available from CRAN. The package implements recently developed kernel methods that seamlessly handle the mix of continuous, unordered, and ordered factor datatypes often found in applied settings. The package also allows users to create their own nonparametric/semiparametric routines using high-level function calls (via the function npksum()) rather than writing their own C or Fortran code. Much of the code underlying the package is written in C including the function npksum(). Currently, a range of methods can be found in the package including - multivariate nonpa...

...ized linear mixed model in R, basically a Poisson model to describe monthly series of counts in different regions. My aim is to fit subject-specific curves, modelling a non-linear trend for each region through random effects for linear splines components (see Durban et al, Stat Med 2005, or " Semiparametric regression" by Ruppert et al, 2003). I use the command 'glmmPQL' in the MASS package and replicated the analysis with Stata's 'xtmepoisson'. I obtained very different results, so I would like to try 'glmer' in the lme4 package. I guess the default correlation fo...

Dear all, I would like to create a code for semiparametric Klein and Spady's estimator. For that I created a function that provides the log-likelihood function for each observation (so it is a function of betas and i, where i denotes the observation). Now, in order to maximize the log-likelihood function, I have to sum these log-likelihood functions...

...{it} - \bar{e}_i Fitting the demeaned model should give me coefficient estimates equal to the non de-meaned model, including coefficient estimates on the spline terms. However, there is certainly autocorrelation in the errors, and potentially heteroskedasticity. The Ruppert et al textbook on semiparametric regression uses GLS to account for correlated errors. I haven't really used GLS much and I don't think it solves the autocorrelation problem. I'm more accustomed to using a cluster-robust "sandwich" estimator: (X'X)^{-1} (sum_j(X_j' e_j e_j' X_j)) (X'...

...ate simulated Y variables that can control the proportion of zeros after generating variables x1, x2, x3 on ZIP analysis.Thus, I can examine more deeply to determine how much the proportion of zeros on response variable (Y) that can be used in the Poisson regression analysis, parametric ZIP and ZIP semiparametric. syntax that I made previously by generating variable y without being controlled to produce zero excess in R : > b0=1.5 > b1=-log(2) > b2=log(3) > b3=log(4) > n=100 > x1<-rnorm(n, mean=5, sd=2) > x2<-runif(n, min=1, max=2) > x3<-rnorm(n, mean=10, sd=15) > &g...

Hi there, I am reading the 2004 paper "Smoothing with mixed model software" in Journal of Statistical Software, by Ngo and Wand. I tried to run their first example in Section 2.1 using R but I had some problems. Here is the code: library(nlme) fossil <- read.table("fossil.dat",header=T) x <- fossil$age y <- 100000*fossil$strontium.ratio knots <-