Search results for "soft"

Outlier detection with automatic modelling: TRAMO/SEATS versus X-12-ARIMA

2012

What subject matter questions motivate the use of machine learning approaches compared to statistical models for probability prediction?

2014

This is a discussion of the following papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gerard Biau, Michael Kohler, Inke R. Konig, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. Konig, and Andreas Ziegler.

Nucleation phenomena in polymeric systems

1995

Materials formed from long flexible macromolecules differ from their small-molecule analogs, because corresponding collective length scales are distinctly larger and many dynamical phenomena are very much slower; in addition, the variation of chain length N yields a control parameter that leaves intermolecular forces invariant, but allows a stringent test of theories. These concepts are exemplified in a discussion of nucleation barriers for symmetrical polymer (A, B)-mixtures (chain lengths NA = NB = N) near the critical temperature Tc, and for symmetrical block copolymers near the (fluctuation-induced) first order transition between the disordered melt and the lamellar mesophase. While in …

Monte Carlo simulation of the glass transition in three-dimensional dense polymer melts

1993

Abstract We determine the incoherent intermediate scattering function φsq(t) for a three-dimensional dense polymer melt. This function shows the signature of a two-step process which was quantitatively compared to the idealized mode coupling theory (MCT) within the β-relaxation regime. A major result of this analysis is that the studied temperature interval splits in a high temperature part, where the idealized theory describes φsq(t) over about three decades in time, and a low temperature part, where it strongly overestimates the freezing tendency of the melt. Since one can qualitatively attribute this discrepancy between the idealized MCT and the simulation data to hopping processes, the …

Aging effects in glassy polymers: a Monte Carlo study

1996

Abstract By means of dynamic Monte Carlo simulation the physical aging of a glassy polymer melt is studied. The melt is simulated by a coarse-grained lattice model, the bond-fluctuation model, on a simple cubic lattice. In order to generate glassy freezing an energy is associated with long bonds, which leads to a competition between the energetically favored bond stretching and the local density of the melt at low temperatures. The development of this competition during the cooling process strongly slows down the structural relaxation and makes the melt freeze in an amorphous structure as soon as the internal relaxation time matches the time scale of the cooling rate. Therefore the model ex…

cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values

2023

Sparse graphical models have revolutionized multivariate inference. With the advent of high-dimensional multivariate data in many applied fields, these methods are able to detect a much lower-dimensional structure, often represented via a sparse conditional independence graph. There have been numerous extensions of such methods in the past decade. Many practical applications have additional covariates or suffer from missing or censored data. Despite the development of these extensions of sparse inference methods for graphical models, there have been so far no implementations for, e.g., conditional graphical models. Here we present the general-purpose package cglasso for estimating sparse co…

Efficient change point detection in genomic sequences of continuous measurements

2010

Abstract Motivation: Knowing the exact locations of multiple change points in genomic sequences serves several biological needs, for instance when data represent aCGH profiles and it is of interest to identify possibly damaged genes involved in cancer and other diseases. Only a few of the currently available methods deal explicitly with estimation of the number and location of change points, and moreover these methods may be somewhat vulnerable to deviations of model assumptions usually employed. Results: We present a computationally efficient method to obtain estimates of the number and location of the change points. The method is based on a simple transformation of data and it provides re…

Contributed discussion on article by Pratola

2016

The author should be commended for his outstanding contribution to the literature on Bayesian regression tree models. The author introduces three innovative sampling approaches which allow for efficient traversal of the model space. In this response, we add a fourth alternative.

Systematic handling of missing data in complex study designs : experiences from the Health 2000 and 2011 Surveys

2016

We present a systematic approach to the practical and comprehensive handling of missing data motivated by our experiences of analyzing longitudinal survey data. We consider the Health 2000 and 2011 Surveys (BRIF8901) where increased non-response and non-participation from 2000 to 2011 was a major issue. The model assumptions involved in the complex sampling design, repeated measurements design, non-participation mechanisms and associations are presented graphically using methodology previously defined as a causal model with design, i.e. a functional causal model extended with the study design. This tool forces the statistician to make the study design and the missing-data mechanism explicit…

A semiparametric approach to estimate reference curves for biophysical properties of the skin

2006

Reference curves which take one covariable into account such as the age, are often required in medicine, but simple systematic and efficient statistical methods for constructing them are lacking. Classical methods are based on parametric fitting (polynomial curves). In this chapter, we describe a new methodology for the estimation of reference curves for data sets, based on nonparametric estimation of conditional quantiles. The derived method should be applicable to all clinical or more generally biological variables that are measured on a continuous quantitative scale. To avoid the curse of dimensionality when the covariate is multidimensional, a new semiparametric approach is proposed. Th…