We analyze the effects on prediction intervals of fitting ARIMA models to series with stochastic trends, when the underlying components are heteroscedastic. We show that ARIMA prediction intervals may be inadequate when only the transitory component is heteroscedastic. In this case, prediction intervals based on the unobserved component models tend to the homoscedastic intervals as the predicti...

Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap m...

We first consider a Bayesian formalism in the wavelet domain that gives rise to the regularised linear wavelet estimator obtained in the standard nonparametric regression setting when the unknown response function belongs to a Sobolev space with non-integer regularity s > 1/2. We then use the posterior distribution of the wavelets coefficients to construct pointwise Bayesian credible intervals ...

We present a method based on the two-process model of sleep regulation for developing individualized biomathematical models that predict performance impairment for individuals subjected to total sleep loss. This new method advances our previous work in two important ways. First, it enables model customization to start as soon as the first performance measurement from an individual becomes avail...

Bayesian highest posterior density (HPD) intervals can be estimated directly from simultions via empirical shortest intervals. Unfortunately, these can be noisy (that is, have a high Monte Carlo error). In this paper, we propose an optimal weighting strategy using quadratic programming to obtain a more computationally stable HPD, or in general, shortest probability interval (Spin). We prove the...

In this article, we adopted the classical and Bayesian approach to develop problem of estimation prediction inverse Lomax distribution under Type-I hybrid censored scheme. Firstly, presented maximum likelihood estimators Bayes unknown parameters consideration squared error loss equation. approach, used Markov chain Monte-Carlo method by applied importance sampling technique. Asymptotic confiden...

Formulating and evaluating trust is important for ensuring security and collaboration among the nodes in MANETs. The dynamic nature of mobile ad hoc networks may contribute to uncertainty in trust opinions. Uncertainty in trust opinions reflects the sufficiency of trust information obtained by a trustor node so that it can accurately compute the trust values of its neighboring nodes. Uncertaint...

Short-term traffic flow prediction has long been regarded as a critical concern for intelligent transportation systems. On the basis of many existing prediction models, each having good performance only in a particular period, an improved approach is to combine these single predictors together for prediction in a span of periods. In this paper, a neural network model is introduced that combines...