[Yuima-commits] r803 - pkg/yuima/man

[noreply at r-forge.r-project.org]

Author: hirokimasuda
Date: 2022-06-23 10:33:59 +0200 (Thu, 23 Jun 2022)
New Revision: 803

Modified:
   pkg/yuima/man/fitCIR.Rd
Log:
modified fitCIR.Rd

Modified: pkg/yuima/man/fitCIR.Rd
===================================================================
--- pkg/yuima/man/fitCIR.Rd	2022-06-23 08:32:56 UTC (rev 802)
+++ pkg/yuima/man/fitCIR.Rd	2022-06-23 08:33:59 UTC (rev 803)
@@ -1,59 +1,60 @@
-% Generated by roxygen2
-\name{fitCIR}
-\alias{fitCIR}
-
-\title{Calculate preliminary estimator and one-step improvements of a Cox-Ingersoll-Ross diffusion}
-
-\description{
-This is a function to simulate the preliminary estimator and the corresponding one step estimators based on the Newton-Raphson and the scoring method of the Cox-Ingersoll-Ross process given via the SDE 
-
-	\eqn{\mathrm{d} X_t = (\alpha-\beta X_t)\mathrm{d} t + \sqrt{\gamma X_t}\mathrm{d} W_t}
-		
-with parameters \eqn{\beta>0,} \eqn{2\alpha>5\gamma>0} and a Brownian motion \eqn{(W_t)_{t\geq 0}}. This function uses the Gaussian quasi-likelihood, hence requires that data is sampled at high-frequency.
-}
-
-\usage{
-fitCIR(data)
-}
-
-\arguments{
-  \item{data}{
-  a numeric matrix 
-containing the realization of \eqn{(t_0,X_{t_0}), \dots,(t_n,X_{t_n})} with \eqn{t_j} denoting the \eqn{j}-th sampling times. \code{data[1,]} contains the sampling times \eqn{t_0,\dots, t_n} and \code{data[2,]} the corresponding value of the process \eqn{X_{t_0},\dots,X_{t_n}.} In other words \code{data[,j]=}\eqn{(t_j,X_{t_j})}. The observations should be equidistant. 
-  }
-}
-
-\value{
-  A list with four entries. The first three entries each contain a vector in the following order: The result of the preliminary estimator, Newton-Raphson method and the method of scoring. The last entry contains the model,	an object of \code{\link{yuima.model-class}}.
-  
-  If the sampling points are not equidistant the function will return \code{'Please use equidistant sampling points'.}
-
-}
-
-\details{
-The estimators calculated by this function can be found in the reference below.
-}
-
-\references{
-Y. Cheng, N. Hufnagel, H. Masuda. Estimation of ergodic square-root diffusion under high-frequency sampling. Econometrics and Statistics, Article Number: 346 (2022).
-}
-
-\author{
-Nicole Hufnagel 
-
-Contacts: \email{nicole.hufnagel at math.tu-dortmund.de}
-}
-
-\examples{
-#You can make use of the function simCIR to generate the data 
-data <- simCIR(alpha=3,beta=1,gamma=1, n=5000, h=0.05, equi.dist=TRUE)
-results <- fitCIR(data)
-}
-
-\keyword{CIR diffusion, high-frequency sampling}
-
-
-
-
-
-
+% Generated by roxygen2
+\name{fitCIR}
+\alias{fitCIR}
+
+\title{Calculate preliminary estimator and one-step improvements of a Cox-Ingersoll-Ross diffusion}
+
+\description{
+This is a function to simulate the preliminary estimator and the corresponding one step estimators based on the Newton-Raphson and the scoring method of the Cox-Ingersoll-Ross process given via the SDE 
+
+	\eqn{\mathrm{d} X_t = (\alpha-\beta X_t)\mathrm{d} t + \sqrt{\gamma X_t}\mathrm{d} W_t}
+		
+with parameters \eqn{\beta>0,} \eqn{2\alpha>5\gamma>0} and a Brownian motion \eqn{(W_t)_{t\geq 0}}. This function uses the Gaussian quasi-likelihood, hence requires that data is sampled at high-frequency.
+}
+
+\usage{
+fitCIR(data)
+}
+
+\arguments{
+  \item{data}{
+  a numeric matrix 
+containing the realization of \eqn{(t_0,X_{t_0}), \dots,(t_n,X_{t_n})} with \eqn{t_j} denoting the \eqn{j}-th sampling times. \code{data[1,]} contains the sampling times \eqn{t_0,\dots, t_n} and \code{data[2,]} the corresponding value of the process \eqn{X_{t_0},\dots,X_{t_n}.} In other words \code{data[,j]=}\eqn{(t_j,X_{t_j})}. The observations should be equidistant. 
+  }
+}
+
+\value{
+  A list with three entries each contain a vector in the following order: The result of the preliminary estimator, Newton-Raphson method and the method of scoring.
+ % A list with four entries. The first three entries each contain a vector in the following order: The result of the preliminary estimator, Newton-Raphson method and the method of scoring. The last entry contains the model,	an object of \code{\link{yuima.model-class}}.
+  
+  If the sampling points are not equidistant the function will return \code{'Please use equidistant sampling points'.}
+
+}
+
+\details{
+The estimators calculated by this function can be found in the reference below.
+}
+
+\references{
+Y. Cheng, N. Hufnagel, H. Masuda. Estimation of ergodic square-root diffusion under high-frequency sampling. Econometrics and Statistics, Article Number: 346 (2022).
+}
+
+\author{
+Nicole Hufnagel 
+
+Contacts: \email{nicole.hufnagel at math.tu-dortmund.de}
+}
+
+\examples{
+#You can make use of the function simCIR to generate the data 
+data <- simCIR(alpha=3,beta=1,gamma=1, n=5000, h=0.05, equi.dist=TRUE)
+results <- fitCIR(data)
+}
+
+\keyword{CIR diffusion, high-frequency sampling}
+
+
+
+
+
+