In this paper, the nonlinear non-Gaussian filters and smoothers are proposed using the joint density of the state variables, where the sampling techniques such as rejection sampling (RS), importance resampling (IR) and the MetropolisHastings independence sampling (MH) are utilized. Utilizing the random draws generated from the joint density, the density-based recursive algorithms on filtering a...

Mediation is a causal process that evolves over time. Thus, a study of mediation requires data collected throughout the process. However, most applications of mediation analysis use cross-sectional rather than longitudinal data. Another implicit assumption commonly made in longitudinal designs for mediation analysis is that the same mediation process universally applies to all members of the po...

This paper surveys some common state space models used in macroeconomics and finance and shows how to specify and estimate these models using the SsfPack algorithms implemented in the S-PLUS module S+FinMetrics. Examples include recursive regression models, time varying parameter models, exact ARMA models and calculation of the Beveridge-Nelson decomposition, unobserved components models, stoch...

Most models that explain observations in time depend on a structured state space as a basis for their modeling. We present methods to derive such a state space and its dynamics automatically from the observations, without any knowledge of their meaning or source. First, we build an explicit state space from an observation history in an off-line fashion (OFESI) by starting with the space induced...

ABSTRACT: In this paper, a framework for studying the dynamics of systems with frequency dependent parameters, under stochastic loads, using the state-space modeling approach is presented. Particular emphasis is placed on the stochastic buffeting response of long span bridges under multicorrelated wind excitation. The buffeting response is expressed as the output of an integrated system driven ...

Design-oriented analysis of switching converters must take into account how disturbances and variations in the input and control signals affect the optimum performance of these converters. Sate-space averaging of a nonideal boost converter is presented in this paper. DC state and small signal AC state modeling of the converter was derived. Determination of line-to-output transfer function, whic...

The paper refers to problems of modeling and computer simulation of generic memristors caused by the so-called window functions, namely the stick effect, nonconvergence, and finding fundamentally incorrect solutions. A profoundly different modeling approach is proposed, which is mathematically equivalent to windowbased modeling. However, due to its numerical stability, it definitely smoothes th...

Linear State Space Modeling determines the hidden autoregressive (AR) process in a noisy time series; for an AR process the time series’ current value is the sum of current stochastic “noise” and a linear combination of previous values. We present preliminary results from modeling a sample of 4 channel BATSE LAD lightcurves. We determine the order of the AR process necessary to model the bursts...

Stochastic modeling forms the basis for analysis in many areas including biological and economic systems as well as the per formance and reliability modeling of computers and communication net works One common approach is the state space based technique which starting from a high level model uses depth rst search to generate both a description of every possible state of the model and the dynami...