search for: beta_t

...n analysis. The following is the 'hierarchy' of models I would like to test: (1) Y_i = a + integral[ X_i(t)*Beta(t) dt ] (2) Y_i = a + integral[ F{X_i(t)}*Beta(t) dt ] (3) Y_i = a + integral[ F{X_i(t),t} dt ] equivalents for discrete data might be: 1) Y_i = a + sum_t[ L_t * X_it * Beta_t ] (2) Y_i = a + sum_t[ L_t * F{X_it} * Beta_t ] (3) Y_i = a + sum_t[ L_t * F{X_it,t} ] where Y_i are scalar outcomes for the i-th subject, and X_i(t) is a functional covariate observed at times t in [0,1,...T], and L are the quadrature weights. Beta() and/or F{} are the functions to be estima...

...m, I apologize for any cross-posting. I got a simple question, which I thought the R list may help me to find an answer. Suppose we have Y_1, Y_2, ., Y_n ~ Poisson (Lambda_i) and Lambda_i ~Gamma(alpha_i, beta_i). Empirical Bayes Estimator for hyper-parameters of the gamma distr, i.e. (alpha_t, beta_t) are needed. y=c(12,5,17,14) n=4 What about a Hierarchal B ayes estimators? Any relevant work and codes in R (or S+) is highly appreciated. Kind regards, Ali [[alternative HTML version deleted]]

...RMA model where the variance of the disturbances is a function of some exogenous variable. So something like: Y_t = a_0 + a_1 * Y_(t-1) +...+ a_p * Y_(t-p) + b_1 * e_(t-1) +...+ b_q * e_(t-q) + e_t, where e_t ~ N(0, sigma^2_t), and with the variance specified by something like sigma^2_t = exp(beta_t * X_t), where X_t is my exogenous variable. I would be very grateful if somebody could point me in the direction of a library that could fit this (or a similar) model. Thanks, James Kirkby Actuarial Maths and Stats Heriot Watt University

...del, If I am trying to do a simple regression where I assume the intercept is constant and the 'Beta' is changing, how do I do that? How do i Initialize the filter (i.e. what is appropriate to set m0, and C0 for the example below)? The model I want is: y = alpha + beta + err1; beta_(t+1) = beta_t + err2 I thought of the following: library(mvtnorm) # (1) library(sspir) # Let's get some data so we can all try this at home dfrm <- data.frame( y = c(0.02,0.04,-0.03,0.02,0,0.01,0.04,0.03,-0.01,0.04,-0.01,0.05,0.04, 0.03,0.01,-0.01,-0.01,-0.03...