Displaying 20 results from an estimated 1000 matches similar to: "statistics - hypothesis testing question"
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
2008 Jul 21
2
avoid loop with three-dimensional array
Dear R user,
I'm trying to find a solution for optimizing my code. I have to run a 50.000
iteration long simulation and it is absolutely necessary to have an
optimized code.
I have to do this operation
*sum_t ( t(X_t) %*% A %*% X_t )*
where X_t is a (d*k) matrix which changes in time and A is a constant in
time (d*d) matrix.
I have put all my X_t in a three dimensional array X of dimension
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
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
2017 Jun 20
1
How to write an estimated seasonal ARIMA model from R output?
I'm trying to use the following command.
arima (x, order = c(p,d,q), seasonal =list(order=c(P,D,Q), period=s)
How can I write an estimated seasonal ARIMA model from the outputs. To be specifically, which sign to use? I know R uses a different signs from S plus.
Is it correct that the model is:
(1-ar1*B-ar2*B^2-...)(1-sar1*B^s-sar2*B^2s-....)(1-B)^d(1-B^s)^D
2007 Aug 28
1
FW: How to fit an linear model withou intercept
Hi Mark,
I don't know wether you recived a sufficient reply or not, so here are
my comments to your problem.
Supressing the constant term in a regression model will probably lead to
a violation of the classical assumptions for this model.
From the OLS normal equations (in matrix notation)
(1) (X'X)b=X'y
and the definition of the OLS residuals
(2) e = y-Xb
you get - by
2007 Jul 06
1
algebra/moving average question - NOTHING TO DO WITH R
This has ABSOLUTELY nothing to do with R but I was hoping that someone
might know because there are obviously a lot of very bright people on
this list.
Suppose I had a time series of data and at each point in time t, I was
calculating x bar + plus minus sigma where x bar was based on a
moving window of size n and so was sigma.
So, if I was at time t , then x bar t plus minus sigma_t would be
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
2006 Feb 06
1
marginal distribution wrt time of time series ?
Dear all,
In many papers regarding time series analysis
of acquired data, the authors analyze 'marginal
distribution' (i.e. marginal with respect to time)
of their data by for example checking
'cdf heavy tail' hypothesis.
For i.i.d data this is ok, but what if samples are
correlated, nonstationary etc.?
Are there limit theorems which for example allow
us to claim that
2007 Mar 05
1
Heteroskedastic Time Series
Hi R-helpers,
I'm new to time series modelling, but my requirement seems to fall just
outside the capabilities of the arima function in R. I'd like to fit an
ARMA 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),
2007 Aug 07
1
Functions for autoregressive Regressionmodels (Mix between times series and Regression Models) ?
Hello everybody,
I've a question about "autoregressive Regressionmodels".
Let Y[1],.....,Y[n], be a time series.
Given the model:
Y[t] = phi[1]*Y[t-1] + phi[2]*Y[t-1] + ... + phi[p]*Y[t-p] + x_t^T*beta + u_t,
where x_t=(x[1t],x[2t],....x[mt]) and beta=(beta[1],...,beta[m]) and u_t~(0,1)
I want to estimate the coefficients phi and beta.
Are in R any functions or packages for
2008 Jul 22
1
help with simulate AR(1) data
Hi, sorry for bothering your guys again.
I want to simulate 100 AR(1) data with cor(x_t, x_t-1)=rho=0.3. The mean of
the first 70 data (x_1 to x_70) is 0 and the mean of the last 30 data (x_71
to x_100) is 2. Can I do it in the following way?
x <- arima.sim(list=(ar=0.3), 100)
mean <- c(rep(0, 70), rep(2, 30))
xnew <- x+mean
If the above code to simulate 100 AR(1) data is right, what
2004 Nov 20
1
annotation problems (conditional text())
Hello,
I'm trying to annotate my plots nicely and am running into trouble.
This example contains two problems:
a) the 'text()' arguments do not show the conditional behavior I'm
trying to give them. I try to test for the intercept of my regression
and reformat the output accordingly ('+ intercept' in the >= 0 case and
'- sqrt(intercept^2)' in the other case),
2010 Aug 13
2
How to compare the effect of a variable across regression models?
Hello,
I would like, if it is possible, to compare the effect of a variable across
regression models. I have looked around but I haven't found anything. Maybe
someone could help? Here is the problem:
I am studying the effect of a variable (age) on an outcome (local recurrence:
lr). I have built 3 models:
- model 1: lr ~ age y = \beta_(a1).age
- model 2: lr ~ age + presentation
2012 Jan 17
2
result numeric(0) when using variable1[which(variable2="max(variable2)"]
Dear all,
I have a question about the knowing for which row I have the max value of
one of my variables.
I calculated the Rsquared for different columns and made a list to gather
them. I unlisted this list to create a vector with this values. I want to
know for which column I have the max value of Rsquared.
The columns were always named in the same way. They always start with
results4$depth_
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
2002 Apr 09
2
Restricted Least Squares
Hi,
I need help regarding estimating a linear model where restrictions are imposed on the coefficients. An example is as follows:
Y_{t+2}=a1Y_{t+1} + a2 Y_t + b x_t + e_t
restriction
a1+ a2 =1
Is there a function or a package that can estimate the coefficient of a model like this? I want to estimate the coefficients rather than test them.
Thank you for your help
Ahmad Abu Hammour
--------------
2012 Feb 16
1
how to get r-squared for a predefined curve or function with "other" data points
hello mailing list!
i still consider myself an R beginner, so please bear with me if my
questions seems strange.
i'm in the field of biology, and have done consecutive hydraulic
conductivity measurements in three parallels ("Sample"), resulting in three
sets of conductivity values ("PLC" for percent loss of conductivity,
relative to 100%) at multiple pressures
2011 Apr 15
3
Rsquared for anova
I calculate an anova test in the following way:
expdata<-read.table("/home/dorien/UA/meta-music/optimuse/optimuse1-build-desktop/results/results_processedCP",
header=TRUE)
2012 Oct 11
2
ccf(x,y) vs. cor() of x and lagged values of y
Hi
I'm computing the correlation between two time-series x_t and y_t-1
(time-series lagged using the lag(y,-1) function) using the cor() function
and the returned value is different from the value of ccf() function at the
same lag. Any ideas why this is so?
Thanks in advance for any hints.
Mihnea
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