Regression Analysis for Time-Varying Variables
Time-varying sparsity in dynamic regression models
... small regression effects to be aggressively shrunk towards zero under the posterior (see Carvalho et ...of regression coefficients which are sparse (that is most of the regres- sion coefficients have values ...
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On the Nuisance of Control Variables in Regression Analysis
... control variables is common however in empir- ical ...“control variables have expected signs" or “it is worth noting the results of our control ...parametric regression models also explicitly ...
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Time varying covariates and coefficients in Cox regression models
... explore time-varying coefficient One way to model time-varying coefficients is to use a step function, ...the analysis time into several intervals and Cox proportional model is ...
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Multiple regression analysis using climate variables
... picked variables that are significant for the model and produce the best ...of time is also not important in this case because there is no effect of lag based on both ...independent variables did not ...
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Bayesian Markov switching tensor regression for time-varying networks
... over time, we consider a subsample of length T = 110 months (from December 2003 to January 2013) made of 61 financial ...a time series of networks in several ...whole time series of ...twelve ...
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Kernel-based Inference in time-varying coefficient cointegrating regression
... with time-varying coefficients and multiple nonsta- tionary regressors using classic kernel smoothing methods to estimate the coefficient ...kernel regression to take account of practical features of ...
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Survival Analysis of Patients with Heart Failure: Implications of Time-Varying Regression Effects in Modeling Mortality
... two time-varying regression models. From graphical analysis of the Cox-Snell residuals displayed in Figures 4A and 4B a moderate deviation from the unit exponential distribution can be ...
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Semiparametric time-varying coefficient regression model for longitudinal data with censored time origin
... after time point ...the time points in the interval ...that time we do not have enough information to get the estimator of the ...since time is always nonnegative, the probability of S i ...
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Analysis of time varying parameter models
... reasonable choice of m there is a strong possibility that the transient actually occurred at time t . However the estimation of d, if d is greater than 1, is not easy. This should be done by trial and error ...
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Model switching and model averaging in time-varying parameter regression models
... 4 Conclusions The Bayesian empirical researcher often faces a trade-o¤ between the desire to work with a fully speci…ed Bayesian model and the computational burden that use of MCMC methods imposes. In the DMA literature, ...
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Model Switching and Model Averaging in Time-Varying Parameter Regression Models
... retrospective analysis where the researcher looks back on past forecast ...in time t (given information available at time t) would have thought were good ...the time series ...
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Model switching and model averaging in time-varying parameter regression models
... 4 Conclusions The Bayesian empirical researcher often faces a trade-o¤ between the desire to work with a fully speci…ed Bayesian model and the computational burden that use of MCMC methods imposes. In the DMA literature, ...
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First Birth Interval: Cox Regression Model with Time Varying Covariates
... individual. Regression models for time to event data have been based on the Cox regression model, which assumes that the underlying hazard function for any two levels of some covariates is ...
7
Multiple imputation in Cox regression when there are time-varying effects of covariates.
... Cox regression in which both the functional form for continuous covariates and the functional form for TVEs of covariates are modelled using frac- tional polynomials (the MFPT ...of time-varying and ...
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Regression Analysis with Block Missing Values and Variables Selection
... a regression model when a block of observations is missing, ...explanatory variables or covariates observed and another set of observations with only a block of the variables ...the regression ...
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Bayesian Analysis of Time-Varying Parameter Vector Autoregressive Model with the Ordering of Variables for the Japanese Economy and Monetary Policy
... Institute of Economic Research Hitotsubashi University 2-1 Naka, Kunitatchi Tokyo, 186-8601 Japan http://gcoe.ier.hit-u.ac.jp. Global COE Hi-Stat Discussion Paper Series[r] ...
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A Measure of Variability in Comovement for Economic Variables : a Time-Varying Coherence Function Approach
... Based on the assumption of local stationarity, this literature has been extended to the Wigner-Ville distribution, namely the ’Short time Fourier transform’ (STFT). The frequency domain approach improves our ...
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Time-varying Reeb graphs : a topological framework supporting the analysis of continuous time-varying data
... 3.3 Conclusions Isocontour extraction algorithms for time-varying scalar fields use spatial tech- niques, span space techniques, and topological techniques to increase efficiency. Spatial techniques ...
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Semiparametric Bayesian inference for time-varying parameter regression models with stochastic volatility
... In this paper, we consider two semiparametric extensions of the TVP-SV model, utilising a popular Bayesian prior for modelling unknown distributions, the Dirichlet process (DP) prior (Ferguson, 1973). We first use this ...
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Spurious regression problem in Kalman Filter estimation of time varying parameter models
... the time varying parameter and a fall in the RMSE and the mean value of the state ...between variables is linear, fluctuations of state vector results in small bias in the estimation of the slope ...
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