[PDF] Top 20 Estimation for nonlinear time series models using estimating equations
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Estimation for nonlinear time series models using estimating equations
... In this section we recall Godambe's (1985) theorem on stochastic processes and apply it to obtain optimal estimates for recently proposed nonlinear time series models.. Let {Yt1 td} be a[r] ... See full document
20
Estimating equations for biomarker based exposure estimation under non-steady-state conditions
... exposure estimation using biomarker measurements alone, the models formulated by combin- ing the above equations pose a challenge in that the likelihood equations are difficult to ... See full document
10
Bayesian inference for nonlinear structural time series models
... Table 13 presents the computing times, calculated in the same manner as described above. Here, a clearer difference emerges between the standard particle filter and the ADPF. The estimated computing times of the ADPF are ... See full document
30
Search for Additive Nonlinear Time Series Causal Models
... for estimating contemporaneous linear causal structure from time series data have been developed using multiple conditional independence tests, but no such procedures are available for ... See full document
25
Estimation and identification for vector linear time series models
... to nonlinear constraints. Because the Normal Equations are usually highly nonlinear it may be difficult to obtain explicit expressions for the Gaussian ... See full document
294
A study of estimation procedures for time series models in economics
... ARMAX models when presented with the task of inferring economic relationships from time series data and some mention has been made of possible estimators of the parameters of these ... See full document
356
Maximum likelihood estimation of time series models: the Kalman filter and beyond
... The objective of this chapter is reviewing this algorithm and discussing maximum likelihood infer- ence, starting from the linear Gaussian case and discussing the extensions to a nonlinear and non Gaus- sian ... See full document
31
Some aspects of estimation for vector time series models
... The factorization property in Theorem (6.1.1) is often established by reference to abstract arguments, via the theories and ideas of stochastic processes for instance, and these do not readily lend themselves to ... See full document
193
Identification, Estimation and Specification in a Class of Semi Linear Time Series Models
... autoregressive time series models. Such a class of nonstationary models, along with a class of nonstationary threshold models proposed in Gao, Tjøstheim and Yin (2012), may provide ... See full document
21
Recursive estimation of non-linear time series models
... A r.ecursive scheme for simultaneous optimal estimation of conditional mean and variance in a nonlinear ARCH (autoregressive con- ditional heteroscedastic) model is also proposed.. Keywo[r] ... See full document
17
A flexible approach to parametric inference in nonlinear and time varying time series models
... statistical models which are nonlinear or exhibit structural breaks or time variation in ...or time varying parameter models to examine whether monetary policy rules have changed over ... See full document
38
Brain activity detection by estimating the signal to noise ratio of fMRI time series using dynamic linear models
... fMRI time series analysis, an increase of signal followed by a slow decay to baseline (or inactivity level in the case of resting-state) is generally expected in the active regions of the ...The ... See full document
7
Generalized Estimation of Missing Observations in Nonlinear Time Series Model Using State Space Representation
... a Time Series Model to be used in obtaining optimal estimates of miss- ing ...space models and Kalman filter were used to handle irregularly spaced ...generated using a computer ...used ... See full document
8
Moment Properties And Quadratic Estimating Functions For Integer-Valued Time Series Models
... data using the INGARCH (1,1) model via QEF ...investigated using diagnostic tools based on the Pearson ...other estimation methods such as Kalman filter can be ... See full document
19
Dimensionality reduction in nonparametric conditional density estimation with applications to nonlinear time series
... When one has a large amount of data, and it is possible to allocate a suitable validation set, it may be most bene…cial to terminate the algorithm based on the out-of-sample perfor- mance on the validation set. However, ... See full document
153
Robust estimation for structural time series models
... the series with only additive outliers , ...other series has noisy disturbances at both the measurement and transition equations, ...the estimation of hyperparameters , the Kalman filter is ... See full document
316
The estimation of parametric change in time-series models
... neither necessary nor appropriate. The Q matrix does not have a p h y s ic a l interpretation, as i t does in the state estimation problem. In the context being considered here, i t may be thought of as a ... See full document
87
Estimation and testing of persistence in nonlinear and cyclical time series
... these series by performing the Dickey- Fuller test with a linear trend for the null hypothesis of a unit root against the alternative of a stationary ...AR(p). Using log-transformation of the data, they ... See full document
253
Gyroscope Random Drift Modeling, using Neural Networks, Fuzzy Neural and Traditional Time- series Methods
... and Time series methods, actual test data about random drift of a DTG has been processed, and the random drift is compensated for and is ...non-linear models are ...the time series are ... See full document
8
Estimation of semiparametric econometric time series models with non linear or heteroscedastic disturbances
... these models, the autoregression function has one singular point at the mean of the process, that is at ...differentiates models 1 and 4 from models 2, 3, 5 and 6 is that the latter models ... See full document
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