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12 views

Parameter estimation using lmom package

As a part of my risk management job, I need to try to fit various distributions to loss data. I have been using the lmom package in R for estimating parameters for ...
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24 views

Optimize parameters?

I've been back-testing a trading strategy that has two parameters (both are # of days to look back), and I've tested the system for robustness by comparing my 10-year Sharpe ratio based on approx ...
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0answers
20 views

Verification of an optimal parameter from an empirical CDF

Suppose we have the following model for the variable $V_5$: $$V_5 = \prod_{k=1}^5(e^{\mu + 0.2X_k}+0.05e^{0.05Y_i - 0.00125}), X_i,Y_i\sim N(0,1)$$ What I wish to do is to solve the problem ...
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27 views

forcing certain parameters to be skipped during optim in R

I have a code which tests each possible order of ARIMA and selects the best model by choosing the one with the absolute minimum sum of lags from the PACF graph. The code then proceeds to add weight to ...
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1answer
69 views

Likelihood ratio test for comparing two exponential distributions

I am trying to use a likelihood ratio test to compare the parameters of two exponential distributions. by this thread Likelihood Ratio for two-sample Exponential distribution I found that I can use ...
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1answer
36 views

Point estimation MLE and MME

Consider the family of probability mass functions given by f(x;k) = 3(4^(k-x)) x = k + 1, k + 2,.... and indexed by parameter k E Z. For a random sample of size n, derive with justification: a) ...
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1answer
29 views

How do models, parameters, specifications, restrictions and assumptions relate?

So this has been something I've been struggling with for a long time: The specification of a particular model is subjective. However, there seems to be objective ('true') values of the parameters we ...
2
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1answer
60 views

Confusing Holt-Winters parameters

I have got a model for forecasting using holt-winters. However the parameters confuse me... The parameters show that there is no trend or seasonality even though there is definite trend and ...
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0answers
22 views

Do assumptions for estimators affect population parameters?

TL;DR: Specifying a model (a collection of restrictions over a sample space) specifies the model parameters. Specifying an estimation procedure adds additional number of restrictions (assumptions?). ...
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1answer
50 views

Calibrating Generalized Hyperbolic distribution in R - which parameters are valid and allow for a numerical calculation of absolute moments

I am using the R-package ghyp in order to calibrate and model. In fact my coding is based on this paper. I know that I could do quite a robust fit using ...
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1answer
26 views

Stable Distribution Log-likelihood and AIC values

I have used the stableFit function from the fBasics package to come up with parameters (alpha, beta, gamma, and delta) for a stable distribution as you can see below: ...
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1answer
27 views

Definition of Parameters [duplicate]

I imagine this either extremely simple or extremely complex. I am trying to understand the interpretation of the term 'parameter'. A couple of quick online searches deliver an intuitive understanding ...
1
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1answer
64 views

Parameters for a Levy distribution in R

Can someone help me figure out how I can get parameter estimates for a levy distribution using R? Unlike the normal distribution and Student T distribution which has functions ...
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0answers
26 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
27 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 ...
1
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1answer
41 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
95 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
23 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
48 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
28 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
52 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
31 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
64 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
24 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
37 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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111 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 ...
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148 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
26 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
17 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
157 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
54 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
40 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
236 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 ...
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1answer
196 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 ...
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0answers
68 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 ...
2
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1answer
1k 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 ...
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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
120 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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1answer
111 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 ...
1
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1answer
331 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
115 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
292 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
167 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 ...
3
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0answers
159 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
357 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
56 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
241 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
343 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
111 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 ...