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18 views

Floor effects in Bayesian estimate, can I reparameterize?

I'm replicating an old study and I have two sets of existing estimates which measure a similar effect, namely the presence of a studied item in memory over time: ...
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1answer
33 views

What do you call a parameter that is estimated from historical values?

There are several methods to estimate parameters in a model (MLE, MAP, GMM). Does the process of estimating a parameter from historical data have a name?
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101 views

expressing this probability distribution over different variables

I have a likelihood function as follows: $$ P(y|x,w, \phi) = \frac{\phi}{2\pi} \exp ^{-0.5 (y-t(x, w)'\phi (y-t(x,w)) } $$ Here $y$ and $x$ are two observed values. $\phi$ is also some given ...
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3answers
85 views

Can you ever have known parameters?

Maybe a bit of a philosophical question - but can you ever truly have known parameters in data? I have a set of data for which the dataset is complete, but the parameters will still be estimates i ...
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0answers
56 views

Rejection sampling from a Gamma distribution using a Cauchy proposal

i'm trying to find the parameters $ \gamma,x_0$ of a standard Cauchy distribution : $$T(x)= \frac{1}{(\pi \gamma (1+(\frac{x-x_0}{\gamma})^2))} $$ To perform rejection sampling from a gamma ...
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0answers
21 views

Is possible to solve this problem with neural network?

I have 2 functions f(t) and g(t). I would like to find the function s(t) that minimize the error |f(s(t))-g(t)|^2 Is it possible to estimate s(t) using neural network? I am new to the field so ...
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0answers
15 views

Multivariate skew normal [duplicate]

In the maximum likelihood estimation of Skew Normal, how does R calculate the mean? You know the formula is \begin{equation} \mu=\frac{ \sum_{i} x_{i} W(x_{i})}{\sum_{i} W(x_{i})} \end{equation}. ...
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1answer
77 views

t-distribution parameter estimation

I know there are already several threads on this, but none seem to explicitly cover what I want. I have a set of financial data (pulled straight from Bloomberg) and am trying to fit a t-distribution ...
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0answers
28 views

What happens with covariates when doing contrasts?

I am doing an analysis of covariance (in SPSS) but can't find anywhere how does SPSS treat the covariates when producing the analysis for the special contrasts I specified. Does it take them at their ...
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0answers
20 views

Expectation-Maximization (EM) method for parameter estimation using fuzzy logic

I am sorry if my question is not fit here. If so, please recommend me the correct forum. I am thinking of estimating a fuzzy model using the EM method. I have a set of observations from a nonlinear ...
1
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1answer
46 views

What is the formula for lognormal hazard?

I'm plotting a bunch of survivor and hazard curves. The lognormal survivor function is: $S(t)=1-\Phi(\frac{log(t)-\mu}{\sigma}) $ Where $\mu$ is the scalar parameter. From a website ...
2
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1answer
93 views

What is the parameterization of exponential distribution for survival in Stata?

I'm new to data analysis so this is kind of a simple question. I would like to understand why I cannot reproduce a survival curve generated by a fitted exponential model from Stata. I use the ...
2
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0answers
45 views

Weibull Parameter Estimation

I am doing a project in which I need to estimate Weibull parameters for car part failures (I know the data follow Weibull). I have data for 1000 cars (part failure data). Now the problem is suppose ...
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0answers
26 views

Data set to probability distribution to maximum likelihood estimation of sigma 1 and sigma 2

I have following 3 two dimensional datasets. Case 1: (Two continuous random variables) A = 1.3, 2.7, 3.9, 4.7, 5.6, 6.3, 7.5, 8.9, 9.1, 10 B = 7.4, 15.3, 24.4, 25.4, 29.6, 32.1, 34.5, 35.7, 27.8, ...
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1answer
261 views

How to compare dbscan clusters / choose epsilon parameter

I am currently trying to make a DBSCAN clustering using scikit learn in python. I would like to compare the different outputs when varying the epsilon parameter in order to choose the right epsilon ...
0
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1answer
29 views

Cross fitting with same params but differents models

What is the best fitting way with 2 variables to explain ($Z_1$ and $Z_2$) depending on the same variables ($X$ and $Y$) and parameters $\theta$ but with differents models ($f$ and $g$)? For ...
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0answers
105 views

Why is one parameter estimate so high in logistic regression?

I am doing logistic regression on a model with a dependent variable of 4 different sizes of fish. I originally tried to do ordinal logistic but I ended up binning responses into "small" and "large" ...
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0answers
33 views

Parameter Estimation

I have the data in the form of $Y \in \mathbb{R}^T$ a time series. For each point in time I have $ m $ real features $ f_i \in \mathbb{R}^m$. I want to use the following model to fit the data $ ...
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1answer
92 views

Parameters and parameter estimation in graphical models

I try to understand parameter estimation and learning problems at Graphical Models, especially in directed ones (Bayesian Networks). But first of all, I try to understand what exactly a parameter ...
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0answers
23 views

Biexponential distributions and parameter estimation

I am currently attempting to a produce a series of half lives for chemical residues. I can get each individual one fine using non-linear regression to estimate the parameters A and k (from $P=A \times ...
1
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1answer
122 views

Matrix Factorization Model for recommender systems how to determine number of latent features?

I am trying to design a matrix factorization technique for a simple user-item, rating recommender system. I have 2 questions about this. First in a simple implementation that I saw of matrix ...
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0answers
29 views

Is there a term for reducing the number of parameters of a distribution in the following way?

Let's say I have a general distribution that can be specified with 100 parameters $a_1,...,a_{100}$. Now, let's say that within this family of distributions, there is a subfamily that can be ...
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0answers
67 views

When do we calculate the population parameter instead of sample statistics?

When do we calculate the population parameter instead of sample statistics? If there is a this kind of case which statistical tool should we use population parameter or sample statistics? A car ...
1
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1answer
150 views

Which distributions are parameterization invariant when based on the Jeffreys prior?

I understand that the Jeffreys prior provides a method for constructing a prior distribution over parameters for a given model (likelihood function) such that the prior distribution is "invariant ...
5
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2answers
118 views

The vcov function cannot be applied?

I originally asked a question about the delta-method in the context of the hyperbolic distribution. I got an answer there, which is useful, except that it says I should apply the ...
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0answers
89 views

Why {glmnet} can be calculated parameters for all category?

For my understanding, multinomial logit model requires to restrict the parameters for one category to zeros. However, package{glmnet} seems to allow different parameters to every class. Could someone ...
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2answers
232 views

What are the implications of a perfect fit model?

I perform logistic regression with a relatively small dataset (N=65), using 12 parameters (11 variables, one constant, no interactions), which results in a perfectly fitting model (in SPSS). I have a ...
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1answer
55 views

Finding optimum point of parameters

I have an algorithm with 3 parameters and sum of these parameters is equal to one; $a_1+a_2+a_3=1$ and each of them must be between $0$ and $1$. I want to find the optimum point for this parameters. ...
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0answers
135 views

Parameters estimation of ODE system

I have all the data and an ODE system of three equations which has 9 unknown coefficients (a1, a2,..., a9). ...
1
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0answers
180 views

Idea of the Nyblom-Hansen test?

The Nyblom-Hansen test gives information about the stability of the estimated parameters in a model. As far as I understand this test, it looks at the score of the ML at evaluates, how near to zero ...
0
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2answers
91 views

Finding a correspondence between time-series elements

My problem deals in particular with time-series data about server performance, but the solution is sure to be applicable to many types of data sets. Pardon me if the answer is well-known; I don't know ...
3
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0answers
84 views

Asymptotic property of tuning parameter in penalized regression

I'm currently working on asymptotic properties of penalized regression. I've read a myriad of papers by now, but there is an essential issue that I cannot get my head around. To keep things simple, ...
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1answer
128 views

Interpreta​tion of main effect when interactio​n term is significan​t (ex. lme)

As an example I use Pinheiro, J. C. & Bates, D. M. 2000. Mixed-effects models in S and S-PLUS. Springer, New York. page 225. Rats whose body mass has been measured are fed by 3 different diets ...
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1answer
32 views

Output of hyperbfit?

I want to fit a hyperbolid distribution to my data, in my notation, I have the density \begin{align*} H(l;\alpha,\beta,\mu,\delta)&=\frac{\sqrt{\alpha^2-\beta^2}}{2\alpha \delta K_1 ...
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1answer
174 views

How do we usually select the best combination of parameters of a machine learning model (for a given dataset)?

Am I wrong, or the standard way of optimizing a machine learning model is by evaluating the algorithm over the (initial) dataset for all possible combinations of parameters, and then pick up the one ...
0
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1answer
65 views

Learning the parameter of linear model

I was reading the slides from the following http://www.slideshare.net/kunegis/searching-microblogs-coping-with-sparsity-and-document-quality In slide 7, the author proposed just a linear model and ...
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1answer
190 views

Mallow's Cp Question

When comparing each individually generated model's $C_p$ to the number of parameters, which number of parameters is the comparison to? Each individual model or the overall number of parameters?
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1answer
78 views

Proportionality in Bayesian Models: What Is Absorbed?

Considering two Bayesian models: Poisson Likelihood & Beta Prior: $p(y|\lambda) \sim \text{Pois}(\lambda)$, $p(\lambda) \sim \text{Be}(a, b)$: $$ p(\lambda|y) \propto ...
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2answers
661 views

Cross validation and parameter optimization

I have a question about the parameter optimization when I use the 10-fold cross validation. I want to ask that whether the parameters should fix or not during every fold's model training , i.e. (1) ...
2
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2answers
703 views

How to use the SD of a normal sampling distribution to specify the gamma prior for the corresponding precision?

The gamma distribution is a commonly used prior distribution for the precision ($1/sd^2$) of a normal distribution in Bayesian hierarchical modeling. I want to use an informed prior for the variance ...
0
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1answer
130 views

Compound Poisson, preliminary work in R

I need help with following programming code: Beforehand: I tried to estimate Poisson-parameters, but M.L.E. and M.C. do not give satisfactory results regarding actual to expected, from actuarial ...
7
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1answer
277 views

Random Forest: what if I know a variable is important

My understanding is the the random forest picks randomly mtry variables to build each decision tree. So if mtry=ncol/3 then each variables will be used on average in 1/3 of the trees. And 2/3 of the ...
7
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1answer
85 views

Parametrizing the Behrens–Fisher distributions

"On the Behrens–Fisher Problem: A Review" by Seock-Ho Kim and Allen S. Cohen Journal of Educational and Behavioral Statistics, volume 23, number 4, Winter, 1998, pages 356–377 I'm looking at this ...
3
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1answer
83 views

Estimating parameters using a different method?

I have a probability distribution which has two parameters $a$ and $b$ I have re-parametrized the distribution such that the new distribution has two parameters $c$ and $d$ where: $c=a$ but $d = ...
1
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1answer
585 views

Re-parameterization of an asymmetric s-shaped function

Is anyone aware of a re-parameterization of any asymmetric s-shaped function (like, but not necessarily the 5 parameter logistic curve), where one of the parameters is the first inflection point of ...
1
vote
1answer
107 views

Normalize a periodic parameter

I am using inverse modelling software (PEST) to estimate a periodic parameter for the direction of anisotropy, $\hat{\theta}$, which is somewhere in $[0^{\circ}, 180^{\circ})$ (i.e., has a wavelength ...
5
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1answer
156 views

Accounting for discrete or binary parameters in Bayesian information criterion

BIC penalizes based on the number of parameters. What if some of the parameters are some sort of binary indicator variables? Do these count as full parameters? But I can combine $m$ binary parameters ...
2
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1answer
132 views

Gaussian process scale targets

I am currently playing around with Gaussian process regression. I discovered some confusing facts that I would like to be answered: When I optimize the parameters like the length and error terms for ...
21
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1answer
2k views

For which distributions are the parameterizations in BUGS and R different?

I have found some distributions for which BUGS and R have different parameterizations: Normal, log-Normal, and Weibull. For each of these, I gather that the second parameter used by R needs to be ...