Add to ORKGA general approach to Bayesian isotonic changepoint problems is developed. Such isotonic changepoint analysis includes trends and other constraint problems and it captures linear, non-smooth as well as abrupt changes. Desired marginal posterior densities are obtained using a Markov chain Monte Carlo method. The methodology is exemplified using one simulated and two real data examples, where it is shown that our proposed Bayesian approach captures the qualitative conclusion about the shape of the trend change.Departamento de Matemátic
Abstract: After a brief review of previous frequentist and Bayesian approaches to multiple change-po...
Isotonic regression is a useful tool to investigate the relationship between a quantitative covariat...
In this work we consider time series with a finite number of discrete point changes. We assume that ...
A change in model parameters over time often characterizes major events. Situations in which this ma...
1 SUMMARY. In many applications, the mean of a response variable can be assumed to be a non-decreasi...
Published ArticleChange-point analysis deals with the situation where an abrupt change has possibly...
Summary. In the restricted parameter estimation, the use of exponential family have been introduced ...
We introduce a procedure for generalized monotonic curve fitting that is based on a Bayesian analysi...
A Bayesian approach is considered to study the change point problems. A hypothesis for testing chang...
Change point problems are referred to detect heterogeneity in temporal or spatial data. They have a...
Recently there has been a keen interest in the statistical analysis of change point detection and es...
summary:A change-point problem is examined from a Bayesian viewpoint, under nonparametric hypotheses...
Change-point models are useful for modeling times series subject to structural breaks. For interpret...
In this thesis, we consider Bayesian inference on the detection of variance change-point models with...
This work is an in-depth study of the change point problem from a general point of view and a furthe...
Abstract: After a brief review of previous frequentist and Bayesian approaches to multiple change-po...
Isotonic regression is a useful tool to investigate the relationship between a quantitative covariat...
In this work we consider time series with a finite number of discrete point changes. We assume that ...
A change in model parameters over time often characterizes major events. Situations in which this ma...
1 SUMMARY. In many applications, the mean of a response variable can be assumed to be a non-decreasi...
Published ArticleChange-point analysis deals with the situation where an abrupt change has possibly...
Summary. In the restricted parameter estimation, the use of exponential family have been introduced ...
We introduce a procedure for generalized monotonic curve fitting that is based on a Bayesian analysi...
A Bayesian approach is considered to study the change point problems. A hypothesis for testing chang...
Change point problems are referred to detect heterogeneity in temporal or spatial data. They have a...
Recently there has been a keen interest in the statistical analysis of change point detection and es...
summary:A change-point problem is examined from a Bayesian viewpoint, under nonparametric hypotheses...
Change-point models are useful for modeling times series subject to structural breaks. For interpret...
In this thesis, we consider Bayesian inference on the detection of variance change-point models with...
This work is an in-depth study of the change point problem from a general point of view and a furthe...
Abstract: After a brief review of previous frequentist and Bayesian approaches to multiple change-po...
Isotonic regression is a useful tool to investigate the relationship between a quantitative covariat...
In this work we consider time series with a finite number of discrete point changes. We assume that ...