[Yuima-commits] r728 - pkg/yuima/man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri Mar 6 04:41:34 CET 2020
Author: yumauehara
Date: 2020-03-06 04:41:33 +0100 (Fri, 06 Mar 2020)
New Revision: 728
Modified:
pkg/yuima/man/qmleLevy.Rd
Log:
fixed
Modified: pkg/yuima/man/qmleLevy.Rd
===================================================================
--- pkg/yuima/man/qmleLevy.Rd 2020-03-05 13:36:59 UTC (rev 727)
+++ pkg/yuima/man/qmleLevy.Rd 2020-03-06 03:41:33 UTC (rev 728)
@@ -1,138 +1,138 @@
-\encoding{UTF-8}
-\name{qmleLevy}
-\alias{qmleLevy}
-\alias{Estimation.LevyIncr}
-\alias{LevySDE}
-%- Also NEED an '\alias' for EACH other topic documented here.
-\title{
-Gaussian quasi-likelihood estimation for Levy driven SDE
-}
-\description{
-Calculate the Gaussian quasi-likelihood and Gaussian quasi-likelihood estimators of Levy driven SDE.
-}
-\usage{
-qmleLevy(yuima, start, lower, upper, joint = FALSE,
-third = FALSE, Est.Incr = "NoIncr",
-aggregation = TRUE)
-}
-\arguments{
- \item{yuima}{a yuima object.}
- \item{lower}{a named list for specifying lower bounds of parameters.}
- \item{upper}{a named list for specifying upper bounds of parameters.}
- \item{start}{initial values to be passed to the optimizer.}
- \item{joint}{perform joint estimation or two stage estimation, by default \code{joint=FALSE}. If there exists an overlapping parameter, \code{joint=TRUE} does not work for the theoretical reason}
- \item{third}{perform third estimation by default \code{third=FALSE}. If there exists an overlapping parameter, \code{third=TRUE} does not work for the
- theoretical reason.}
- \item{Est.Incr}{the qmleLevy returns an object of \code{mle-clas}, by default \code{Est.Incr="NoIncr"}, other options as \code{"Inc"} or \code{"IncrPar"}.}
- \item{aggregation}{If \code{aggregation=TRUE}, the function returns the unit-time Levy increments. If \code{Est.Incr="IncrPar"}, the function estimates Levy parameters using the unit-time Levy increments.}
-}
-\details{
-This function performs Gaussian quasi-likelihood estimation for Levy driven SDE.
-}
-\value{
-\item{first}{estimated values of first estimation (scale parameters)}
-
-\item{second}{estimated values of second estimation (drift parameters)}
-
-\item{third}{estimated values of third estimation (scale parameters)}
-}
-
-\note{
-The function \code{qmleLevy} uses the function \code{qmle} internally.
-It can be applied only for the standardized Levy noise whose moments of any order exist.
-In present \code{yuima} package, birateral gamma (bgamma) process, normal inverse Gaussian (NIG) process, variance gamma (VG) process, and normal tempered stable process are such candidates.
-In the current version, the standardization condition on the driving noise is internally checked only for the one-dimensional noise.
-The standardization condition for the multivariate noise is given in
-
-\href{https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnx5dW1hdWVoYXJhMTkyOHxneDo1OGIxNGQ2YjBlYWIxNzA3}{https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnx5dW1hdWVoYXJhMTkyOHxneDo3ZTdlMTA1OTMyZTBkYjQ2}
-
-or
-
-\href{https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnx5dW1hdWVoYXJhMTkyOHxneDo3ZTdlMTA1OTMyZTBkYjQ2}{https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnx5dW1hdWVoYXJhMTkyOHxneDo3ZTdlMTA1OTMyZTBkYjQ2}.
-
-They also contain more presice explanation of this function.
-}
-\references{
-
-Masuda, H. (2013). Convergence of Gaussian quasi-likelihood random fields for ergodic Levy driven SDE observed at high frequency. The Annals of Statistics, 41(3), 1593-1641.
-
-Masuda, H. and Uehara, Y. (2017). On stepwise estimation of Levy driven
-stochastic differential equation (Japanese) ., Proc. Inst. Statist. Math., accepted.
-}
-\author{
-The YUIMA Project Team
-
-Contacts: Yuma Uehara \email{y-uehara at math.kyushu-u.ac.jp}
-}
-
-
-
-%\seealso{
-%}
+\encoding{UTF-8}
+\name{qmleLevy}
+\alias{qmleLevy}
+\alias{Estimation.LevyIncr}
+\alias{LevySDE}
+%- Also NEED an '\alias' for EACH other topic documented here.
+\title{
+Gaussian quasi-likelihood estimation for Levy driven SDE
+}
+\description{
+Calculate the Gaussian quasi-likelihood and Gaussian quasi-likelihood estimators of Levy driven SDE.
+}
+\usage{
+qmleLevy(yuima, start, lower, upper, joint = FALSE,
+third = FALSE, Est.Incr = "NoIncr",
+aggregation = TRUE)
+}
+\arguments{
+ \item{yuima}{a yuima object.}
+ \item{lower}{a named list for specifying lower bounds of parameters.}
+ \item{upper}{a named list for specifying upper bounds of parameters.}
+ \item{start}{initial values to be passed to the optimizer.}
+ \item{joint}{perform joint estimation or two stage estimation, by default \code{joint=FALSE}. If there exists an overlapping parameter, \code{joint=TRUE} does not work for the theoretical reason}
+ \item{third}{perform third estimation by default \code{third=FALSE}. If there exists an overlapping parameter, \code{third=TRUE} does not work for the
+ theoretical reason.}
+ \item{Est.Incr}{the qmleLevy returns an object of \code{mle-clas}, by default \code{Est.Incr="NoIncr"}, other options as \code{"Inc"} or \code{"IncrPar"}.}
+ \item{aggregation}{If \code{aggregation=TRUE}, the function returns the unit-time Levy increments. If \code{Est.Incr="IncrPar"}, the function estimates Levy parameters using the unit-time Levy increments.}
+}
+\details{
+This function performs Gaussian quasi-likelihood estimation for Levy driven SDE.
+}
+\value{
+\item{first}{estimated values of first estimation (scale parameters)}
+
+\item{second}{estimated values of second estimation (drift parameters)}
+
+\item{third}{estimated values of third estimation (scale parameters)}
+}
+
+\note{
+The function \code{qmleLevy} uses the function \code{qmle} internally.
+It can be applied only for the standardized Levy noise whose moments of any order exist.
+In present \code{yuima} package, birateral gamma (bgamma) process, normal inverse Gaussian (NIG) process, variance gamma (VG) process, and normal tempered stable process are such candidates.
+In the current version, the standardization condition on the driving noise is internally checked only for the one-dimensional noise.
+The standardization condition for the multivariate noise is given in
+
+\href{https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnx5dW1hdWVoYXJhMTkyOHxneDo1OGIxNGQ2YjBlYWIxNzA3}{https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnx5dW1hdWVoYXJhMTkyOHxneDo3ZTdlMTA1OTMyZTBkYjQ2}
+
+or
+
+\href{https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnx5dW1hdWVoYXJhMTkyOHxneDo3ZTdlMTA1OTMyZTBkYjQ2}{https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnx5dW1hdWVoYXJhMTkyOHxneDo3ZTdlMTA1OTMyZTBkYjQ2}.
+
+They also contain more presice explanation of this function.
+}
+\references{
+
+Masuda, H. (2013). Convergence of Gaussian quasi-likelihood random fields for ergodic Levy driven SDE observed at high frequency. The Annals of Statistics, 41(3), 1593-1641.
+
+Masuda, H. and Uehara, Y. (2017). On stepwise estimation of Levy driven
+stochastic differential equation (Japanese) ., Proc. Inst. Statist. Math., accepted.
+}
+\author{
+The YUIMA Project Team
+
+Contacts: Yuma Uehara \email{y-uehara at ism.ac.jp}
+}
+
+
+
+%\seealso{
+%}
\examples{
-\dontrun{
-## One-dimensional case
-dri<-"-theta0*x" ## set drift
-jum<-"theta1/(1+x^2)^(-1/2)" ## set jump
-yuima<-setModel(drift = dri
- ,jump.coeff = jum
- ,solve.variable = "x",state.variable = "x"
- ,measure.type = "code"
- ,measure = list(df="rbgamma(z,1,sqrt(2),1,sqrt(2))")) ## set true model
-n<-3000
-T<-30 ## terminal
-hn<-T/n ## stepsize
-
-sam<-setSampling(Terminal = T, n=n) ## set sampling scheme
-yuima<-setYuima(model = yuima, sampling = sam) ## model
-
-true<-list(theta0 = 1,theta1 = 2) ## true values
-upper<-list(theta0 = 4, theta1 = 4) ## set upper bound
-lower<-list(theta0 = 0.5, theta1 = 1) ## set lower bound
-set.seed(123)
-yuima<-simulate(yuima, xinit = 0, true.parameter = true,sampling = sam) ## generate a path
-start<-list(theta0 = runif(1,0.5,4), theta1 = runif(1,1,4)) ## set initial values
-qmleLevy(yuima,start=start,lower=lower,upper=upper, joint = TRUE)
-
-## Multi-dimensional case
-
-lambda<-1/2
-alpha<-1
-beta<-c(0,0)
-mu<-c(0,0)
-Lambda<-matrix(c(1,0,0,1),2,2) ## set parameters in noise
-
-dri<-c("1-theta0*x1-x2","-theta1*x2")
-jum<-matrix(c("x1*theta2+1","0","0","1"),2,2) ## set coefficients
-
-yuima <- setModel(drift=dri,
- solve.variable=c("x1","x2"),state.variable = c("x1","x2"),
- jump.coeff=jum, measure.type="code",
- measure=list(df="rvgamma(z, lambda, alpha, beta, mu, Lambda
- )"))
-
-n<-3000 ## the number of total samples
-T<-30 ## terminal
-hn<-T/n ## stepsize
-
-sam<-setSampling(Terminal = T, n=n) ## set sampling scheme
-yuima<-setYuima(model = yuima, sampling = sam) ## model
-
-true<-list(theta0 = 1,theta1 = 2,theta2 = 3,lambda=lambda, alpha=alpha,
-beta=beta,mu=mu, Lambda=Lambda) ## true values
-upper<-list(theta0 = 4, theta1 = 4, theta2 = 5, lambda=lambda, alpha=alpha,
-beta=beta,mu=mu, Lambda=Lambda) ## set upper bound
-lower<-list(theta0 = 0.5, theta1 = 1, theta2 = 1, lambda=lambda, alpha=alpha,
-beta=beta,mu=mu, Lambda=Lambda) ## set lower bound
-set.seed(123)
-yuima<-simulate(yuima, xinit = c(0,0), true.parameter = true,sampling = sam) ## generate a path
-plot(yuima)
-start<-list(theta0 = runif(1,0.5,4), theta1 = runif(1,1,4),
-theta2 = runif(1,1,5),lambda=lambda, alpha=alpha,
-beta=beta,mu=mu, Lambda=Lambda) ## set initial values
-qmleLevy(yuima,start=start,lower=lower,upper=upper,joint = FALSE,third=TRUE)
+\dontrun{
+## One-dimensional case
+dri<-"-theta0*x" ## set drift
+jum<-"theta1/(1+x^2)^(-1/2)" ## set jump
+yuima<-setModel(drift = dri
+ ,jump.coeff = jum
+ ,solve.variable = "x",state.variable = "x"
+ ,measure.type = "code"
+ ,measure = list(df="rbgamma(z,1,sqrt(2),1,sqrt(2))")) ## set true model
+n<-3000
+T<-30 ## terminal
+hn<-T/n ## stepsize
+
+sam<-setSampling(Terminal = T, n=n) ## set sampling scheme
+yuima<-setYuima(model = yuima, sampling = sam) ## model
+
+true<-list(theta0 = 1,theta1 = 2) ## true values
+upper<-list(theta0 = 4, theta1 = 4) ## set upper bound
+lower<-list(theta0 = 0.5, theta1 = 1) ## set lower bound
+set.seed(123)
+yuima<-simulate(yuima, xinit = 0, true.parameter = true,sampling = sam) ## generate a path
+start<-list(theta0 = runif(1,0.5,4), theta1 = runif(1,1,4)) ## set initial values
+qmleLevy(yuima,start=start,lower=lower,upper=upper, joint = TRUE)
+
+## Multi-dimensional case
+
+lambda<-1/2
+alpha<-1
+beta<-c(0,0)
+mu<-c(0,0)
+Lambda<-matrix(c(1,0,0,1),2,2) ## set parameters in noise
+
+dri<-c("1-theta0*x1-x2","-theta1*x2")
+jum<-matrix(c("x1*theta2+1","0","0","1"),2,2) ## set coefficients
+
+yuima <- setModel(drift=dri,
+ solve.variable=c("x1","x2"),state.variable = c("x1","x2"),
+ jump.coeff=jum, measure.type="code",
+ measure=list(df="rvgamma(z, lambda, alpha, beta, mu, Lambda
+ )"))
+
+n<-3000 ## the number of total samples
+T<-30 ## terminal
+hn<-T/n ## stepsize
+
+sam<-setSampling(Terminal = T, n=n) ## set sampling scheme
+yuima<-setYuima(model = yuima, sampling = sam) ## model
+
+true<-list(theta0 = 1,theta1 = 2,theta2 = 3,lambda=lambda, alpha=alpha,
+beta=beta,mu=mu, Lambda=Lambda) ## true values
+upper<-list(theta0 = 4, theta1 = 4, theta2 = 5, lambda=lambda, alpha=alpha,
+beta=beta,mu=mu, Lambda=Lambda) ## set upper bound
+lower<-list(theta0 = 0.5, theta1 = 1, theta2 = 1, lambda=lambda, alpha=alpha,
+beta=beta,mu=mu, Lambda=Lambda) ## set lower bound
+set.seed(123)
+yuima<-simulate(yuima, xinit = c(0,0), true.parameter = true,sampling = sam) ## generate a path
+plot(yuima)
+start<-list(theta0 = runif(1,0.5,4), theta1 = runif(1,1,4),
+theta2 = runif(1,1,5),lambda=lambda, alpha=alpha,
+beta=beta,mu=mu, Lambda=Lambda) ## set initial values
+qmleLevy(yuima,start=start,lower=lower,upper=upper,joint = FALSE,third=TRUE)
}
-}
-
-\keyword{qmle}% use one of RShowDoc("KEYWORDS")
-\keyword{Estimation}% __ONLY ONE__ keyword per line
+}
+
+\keyword{qmle}% use one of RShowDoc("KEYWORDS")
+\keyword{Estimation}% __ONLY ONE__ keyword per line
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