Displaying 20 results from an estimated 3000 matches similar to: "likelihood question not so related to R but probably requires the use of R"
2007 May 08
2
statistics/correlation question NOT R question
This is not an R question but if anyone can help me, it's much
appreciated.
Suppose I have a series ( stationary ) y_t and a series x_t ( stationary
)and x_t has variance sigma^2_x and epsilon is normal
(0, sigma^2_epsilon )
and the two series have the relation
y_t = Beta*x_t + epsilon
My question is if there are particular values that sigma^2_x and
sigma^2_epsilon have to take in
2007 Feb 21
1
loops in R help me please
I am trying to make the following Kalman filter equations work and therefore produce their graphs.
v_t=y_t - a_t
a_t+1=a_t+K_t*v_t
F_t=P_t+sigma.squared.epsilon
P_t+1=P_t*(1-K_t)+sigma.squared.eta
K_t=P_t/F_t
Given:
a_1=0,P_1=10^7,sigma.squared.epsilon=15099,
sigma.squared.eta=1469.1
I have attached my code,which of course doesnt work.It produces NAs for the Fs,Ks and the a.
Can somebody tell me
2010 Oct 06
1
dlm package: how to specify state space model?
Dear r-users!
I have another question regarding the dlm package and I would be very
happy if someone could give me a hint!
I am using the dlm package to get estimates for an endogenous rate of
capacity utilization over time. The general form of a state space model
is
(1) b_t = G * b_t-1 + w_t w_t ~ N(0,W)
(2) y_t= A' * x_t + H' * b_t + v_t v_t ~ N(0,V)
(Hamilton 1984: 372)
The
2000 Apr 04
0
stochastic process transition probabilities estimation
Hi all,
I'm new with R (and S), and relatively new to statistics (I'm a
computer scientist), so I ask sorry in advance if my question is silly.
My problem is this: I have a (sample of a) discrete time stochastic
process {X_t} and I want to estimate
Pr{ X_t | X_{t-l_1}, X_{t-l_2}, ..., X_{t-l_k} }
where l_1, l_2, ..., l_k are some fixed time lags. It will be enough for
me to compute
2000 Sep 22
0
what do you do for 2SLS or 3SLS
For 2 or 3 stage least squares, what do you R folks do?
Follow-up question. My student wants to estimate this. 2 variables are
governed by a system of difference equations. His theory is like so.
Y_t and X_t are
state variables, we want estimates for a, g, b, and h.
X_(t+1) = 1 + a X_t + (a/K)* (X_t)^2 - g Y_t X_t
Y_(t+1) = b Y_t + h* X_t * Y_t
K is perhaps something to estimate, but it
2008 Mar 26
0
recursive multivariate filter with time-varying coefficients
Hi,
I've been searching CRAN and the web for a recursive multivariate
filter with time-varying coefficients.
What I mean is the following:
I have a series of square matrices A_t
an initial value vector y_0
and I need to compute
y_t =A_t%*%y_t-1
As these y_t may diverge quickly and/or lead to underflow problems,
the y_t need to be scaled by eg
y_t =y_t/sum(y_t-1)
Is anyone aware
2007 Aug 05
0
null hypothesis for two-way anova
Dear R community,
Confused by some of my lab results I ask for the definition of the null
hypothesis of a two-way analysis of variance in R (anova() and aov()).
Starting with the following model
y = a_i + b_j , i in A and j in B
is the tested null hypothesis
H_0: a_i = 0 for all i in A
or
H_0: a_m = a_n for any m and n in A?
Consequently the same questions for interaction effects.
2009 Jun 19
1
using garchFit() to fit ARMA+GARCH model with exogeneous variables
Hello -
Here's what I'm trying to do. I want to fit a time series y with
ARMA(1,1) + GARCH(1,1), there are also an exogeneous variable x which I
wish to include, so the whole equation looks like:
y_t - \phi y_{t-1} = \sigma_t \epsilon_t + \theta \sigma_{t-1}
\epsilon_{t-1} + c x_t where \epsilon_t are i.i.d. random
variables
\sigma_t^2 = omega + \alpha \sigma_{t-1}^2 + \beta
2008 Sep 10
2
arima and xreg
Dear R-help-archive..
I am trying to figure out how to make arima prediction when I have a
process involving multivariate time series input, and one output time
series (output is to be predicted) .. (thus strictly speaking its an
ARMAX process). I know that the arima function of R was not designed
to handle multivariate analysis (there is dse but it doesnt handle
arma multivariate analysis, only
2013 Jan 11
0
Manual two-way demeaning of unbalanced panel data (Wansbeek/Kapteyn transformation)
Dear R users,
I wish to manually demean a panel over time and entities. I tried to code
the Wansbeek and Kapteyn (1989) transformation (from Baltagi's book Ch. 9).
As a benchmark I use both the pmodel.response() and model.matrix() functions
in package plm and the results from using dummy variables. As far as I
understood the transformation (Ch.3), Q%*%y (with y being the dependent
variable)
2003 Aug 28
2
ks.test()
Dear All
I am trying to replicate a numerical application (not computed on R) from an
article. Using, ks.test() I computed the exact D value shown in the article
but the p-values I obtain are quite different from the one shown in the
article.
The tests are performed on a sample of 37 values (please see "[0] DATA"
below) for truncated Exponential, Pareto and truncated LogNormal
2013 Jun 17
0
Invert a positive definite symmetric Block Toeplitz Matrix
Is there a function in r that let's you efficiently invert a positive
definite symmetric Block Toeplitz matrix? My matrices are the covariance
matrices of observations of a multivariate time series and can be
1000*1000 or larger.
I know the package 'ltsa' which seems to use the Trench algorithm to
compute the inverse of a Toeplitz matrix. I am looking for a so to say
2008 Sep 10
0
FW: RE: arima and xreg
hi: you should probably send below to R-Sig-Finance because there are
some econometrics people over there who could also possibly give you
a good answer and may not see this email ? Also, there's package called
mar ( I think that's the name ) that may do what you want ?
Finally, I don't know how to do it but I think there are ways of
converting a multivariate arima into the
2002 Aug 02
1
Cox regression
Hi!
I would like to do Cox regression using the available routines in
the survival package BUT I want to use an arbitrary link function, i.e.
want to use the model
h(t)=h_0(t)r(beta'z)
with arbitrary function r, instead of
h(t)=h_0(t)exp(beta'z)
Grateful for any comment on this,
Dragi
-----------------------------------------------------------
Dragi Anevski, PhD
Mathematical
2007 May 21
1
Sample correlation coefficient question NOT R question
This is a statistics question not an R question. When calculating the
sample correlation coefficient cor(x_t,y_t) between say
two variables, x_t and y_t t=1,.....n ( one can assume that the
variables are in time but I don't think this really matters
for the question ), does someone know where I can find any piece of
literature that says that each (x_j,y_j) pair has
To be independent from the
2002 Mar 26
3
ks.test - continuous vs discrete
I frequently want to test for differences between animal size frequency
distributions. The obvious test (I think) to use is the Kolmogorov-Smirnov
two sample test (provided in R as the function ks.test in package ctest).
The KS test is for continuous variables and this obviously includes length,
weight etc. However, limitations in measuring (e.g length to the nearest
cm/mm, weight to the nearest
2011 Dec 01
1
Estimation of AR(1) Model with Markov Switching
Dear R users,
I have been trying to obtain the MLE of the following model
state 0: y_t = 2 + 0.5 * y_{t-1} + e_t
state 1: y_t = 0.5 + 0.9 * y_{t-1} + e_t
where e_t ~ iidN(0,1)
transition probability between states is 0.2
I've generated some fake data and tried to estimate the parameters using the
constrOptim() function but I can't get sensible answers using it. I've tried
using
2003 May 07
0
assessing goodness of variance prediction
Dear R-Helpers,
I am looking for ways to assess quality of a predictor of variance of a
random variable. Here a two related, but yet distinct, setups.
1. I observe y_t, t=1,...,T which is normally distributed with unknown
variance v_t (note that the variance is time-dependent). I have two
"predictors" for v_t, dubbed v1_t and v2_t, and I want to tell which
predictor is better. Here
2010 Nov 24
0
Seeking advice on dynamic linear models with matrix state variable.
Hello, fellow R users,
I recently need to estimate a dynamic linear model in the following form:
For the measurement equation:
Y_t = F_t * a_t + v_t
where Y_t is the observation. It is a 1 by q row vector for each t.
F_t is my forecasting variable. It is a 1 by p row vector.
a_t is my state variable. It is a p by q MATRIX of parameters with each column of the matrix being regression
2012 May 02
1
coxph reference hazard rate
Hi,
In the following results I interpret exp(coef) as the factor that multiplies
the base hazard rate if the corresponding variable is TRUE. For example,
when the bucket is ks008 and fidelity <= 3, then the rate, compared to the
base rate h_0(t), is h(t) = 0.200 h_0(t). My question is then, to what case
does the base hazard rate correspond to? I would expect the reference to be
the first