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15 views

cross validation for parameter-tuning a metaheuristic

For a certain problem, I've come up with a novel metaheuristic. The question I'd like to answer is "Does my metaheuristic perform better than previous methods over most problem instances?". My ...
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1answer
12 views

Smoothing parameter for spline curve with duplicate points

I have body mass and age data for a population of individuals. I want to fit a cubic smoothing spline curve to the data. I'm using smooth.spline in R, which warns against using cross-validation to ...
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1answer
37 views

Choosing between two parameters in a model

I have a few parameters that are related (let's call them X1 and X2), and I want to use whichever one will provide the strongest model. The model has many other parameters. Would I simply be able to ...
2
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1answer
70 views

Best statistical notation for expected probability density

Assume that we have two multivariate normal distributions $\mathcal{N}_1 = \mathcal{N}(\mu_1, \Sigma_1)$ and $\mathcal{N}_2 = \mathcal{N}(\mu_2, \Sigma_2)$. We do these two steps: Pick a point, say ...
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1answer
21 views

Tuning paramaters SVM, DT, k-NN, NN

I'm trying to compare the predictive strenght of four different algorithms: support vector machines k-NN decision trees neural networks I've got a few questions concerning the parameter tuning: ...
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0answers
41 views

Assumptions implied by “pairwise marginal” parameterization of MRF

I'm trying to understand the assumptions of different parameterizations in a Markov network. In this case, I'm trying to understand the assumptions (and effects) that result from parameterizing ...
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0answers
18 views

Efficient scale & shape parameter estimation for generalized secant hyperbolic distribution needed

the (symmetric) generalized secant hyperbolic distribution GSHD is very flexible but I found not much at all on how to estimate its 3 parameters. Given the location, I need to obtain scale & shape ...
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2answers
35 views

How many parameters in this specific linear model with interaction?

I have a question where I am not sure about the answer: A linear model has the following characteristics: *A dependent variable ($y$) *One continuous variable ($x_l$), including a ...
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1answer
27 views

Reparametrisation of a model when an interaction is significant to facilitate the interpretation

It is admitted that it is complex to interpret main effects when they are involved in an interaction. Lets take a regular linear model, with two categorical 2 level variables A and B who are ...
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0answers
45 views

Is it reasonable to measure standard deviation from true value rather than mean?

I am evaluating the accuracy of GPS watches, taking many readings over a known distance. I've been calculating standard deviation using the mean reading, but because I know what the reading should be, ...
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0answers
22 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: ...
0
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1answer
35 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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0answers
110 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
86 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 ...
2
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0answers
106 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
25 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
123 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
49 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
37 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 ...
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1answer
144 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
155 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
59 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
29 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
714 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
30 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
116 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
35 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
103 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
51 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
266 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
33 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
88 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 ...
2
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1answer
253 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 ...
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2answers
141 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 ...
2
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0answers
128 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
301 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 ...
0
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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
205 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). ...
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0answers
298 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 ...
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2answers
102 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
105 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, ...
4
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1answer
246 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
33 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 ...
0
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1answer
220 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
votes
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 ...
2
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1answer
200 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?
1
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1answer
82 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
869 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) ...
3
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2answers
919 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 ...