Density of Multivariate Normal Variance Mixtures
Evaluating multivariate normal variance mixture densities (including Student t and normal densities).
dnvmix(x, qmix, loc = rep(0, d), scale = diag(d), factor = NULL, control = list(), log = FALSE, verbose = TRUE,...) dStudent(x, df, loc = rep(0, d), scale = diag(d), factor = NULL, log = FALSE, verbose = TRUE, ...) dNorm(x, loc = rep(0, d), scale = diag(d), factor = NULL, log = FALSE, verbose = TRUE, ...)
x: -matrix of evaluation points.
qmix: specification of the mixing variable ; see pnvmix() for details and examples.
df: positive degress of freedom; can also be Inf in which case the distribution is interpreted as the multivariate normal distribution with mean vector loc and covariance matrix scale).
loc: location vector of dimension ; this equals the mean vector of a random vector following the specified normal variance mixture distribution if and only if the latter exists.
scale: scale matrix (a covariance matrix entering the distribution as a parameter) of dimension ; this equals the covariance matrix of a random vector following the specified normal variance mixture distribution divided by the expecation of the mixing variable if and only if the former exists. Needs to be full rank for the density to exist.
factor: lower triangular matrix
such that factor %*% t(factor) equals scale; note that for performance reasons, this property is not tested. If not provided, factor is internally determined via t(chol()).
control: list specifying algorithm specific parameters; see get_set_param().
log: logical indicating whether the logarithmic density is to be computed.
verbose: logical indicating whether a warning is given if the required precision abstol has not been reached.
...: additional arguments (for example, parameters) passed to the underlying mixing distribution when qmix
is a character string or function.
dnvmix(), dStudent() and dNorm() return a numeric -vector with the computed (log-)density values and attributes "abs. error" and "rel. error"
(containing the absolute and relative error estimates of the of the (log-)density) and "numiter"
(containing the number of iterations).
For the density to exist, scale must be full rank. Internally used is factor, so scale is not required to be provided if factor is given. The default factorization used to obtain factor is the Cholesky decomposition via chol().
dStudent() and dNorm() are wrappers of dnvmix(, qmix = "inverse.gamma", df = df) and dnvmix(, qmix = "constant"), respectively. In these cases, dnvmix() uses the analytical formulas for the density of a multivariate Student t and normal distribution, respectively.
Internally, an adaptive randomized Quasi-Monte Carlo (RQMC) approach is used to estimate the log-density. It is an iterative algorithm that evaluates the integrand at a randomized Sobol' point-set (default) in each iteration until the pre-specified error tolerance control$dnvmix.reltol in the control argument is reached for the log-density. The attribute "numiter" gives the worst case number of such iterations needed (over all rows of x). Note that this function calls underlying C code.
Algorithm specific parameters (such as above mentioned control$dnvmix.reltol) can be passed as a list via the control argument, see get_set_param() for details and defaults.
If the error tolerance cannot be achieved within control$max.iter.rqmc
iterations and fun.eval[2] function evaluations, an additional warning is thrown if verbose=TRUE.
Erik Hintz, Marius Hofert and Christiane Lemieux.
Hintz, E., Hofert, M. and Lemieux, C. (2021), Normal variance mixtures: Distribution, density and parameter estimation. Computational Statistics and Data Analysis 157C, 107175.
Hintz, E., Hofert, M. and Lemieux, C. (2022), Multivariate Normal Variance Mixtures in : The Package nvmix. Journal of Statistical Software, tools:::Rd_expr_doi("10.18637/jss.v102.i02") .
McNeil, A. J., Frey, R. and Embrechts, P. (2015). Quantitative Risk Management: Concepts, Techniques, Tools. Princeton University Press.
pnvmix(), rnvmix(), fitnvmix(), get_set_param().
### Examples for dnvmix() ###################################################### ## Generate a random correlation matrix in three dimensions d <- 3 set.seed(271) A <- matrix(runif(d * d), ncol = d) P <- cov2cor(A %*% t(A)) ## Evaluate a t_{3.5} density df <- 3.5 x <- matrix(1:12/12, ncol = d) # evaluation points dt1 <- dnvmix(x, qmix = "inverse.gamma", df = df, scale = P) stopifnot(all.equal(dt1, c(0.013266542, 0.011967156, 0.010760575, 0.009648682), tol = 1e-7, check.attributes = FALSE)) ## Here is a version providing the quantile function of the mixing distribution qW <- function(u, df) 1 / qgamma(1-u, shape = df/2, rate = df/2) dt2 <- dnvmix(x, qmix = qW, df = df, scale = P) ## Compare stopifnot(all.equal(dt1, dt2, tol = 5e-4, check.attributes = FALSE)) ## Evaluate a normal density dn <- dnvmix(x, qmix = "constant", scale = P) stopifnot(all.equal(dn, c(0.013083858, 0.011141923, 0.009389987, 0.007831596), tol = 1e-7, check.attributes = FALSE)) ## Case with missing data x. <- x x.[3,2] <- NA x.[4,3] <- NA dt <- dnvmix(x., qmix = "inverse.gamma", df = df, scale = P) stopifnot(is.na(dt) == rep(c(FALSE, TRUE), each = 2)) ## Univariate case x.. <- cbind(1:10/10) # (n = 10, 1)-matrix; vectors are considered rows in dnvmix() dt1 <- dnvmix(x.., qmix = "inverse.gamma", df = df, factor = 1) dt2 <- dt(as.vector(x..), df = df) stopifnot(all.equal(dt1, dt2, check.attributes = FALSE)) ### Examples for dStudent() and dNorm() ######################################## ## Evaluate a t_{3.5} density dt <- dStudent(x, df = df, scale = P) stopifnot(all.equal(dt, c(0.013266542, 0.011967156, 0.010760575, 0.009648682), tol = 1e-7, check.attributes = FALSE)) ## Evaluate a normal density x <- x[1,] # use just the first point this time dn <- dNorm(x, scale = P) stopifnot(all.equal(dn, 0.013083858, tol = 1e-7, check.attributes = FALSE))
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