The tag has no wiki summary.

learn more… | top users | synonyms (1)

0
votes
0answers
6 views

Parameterization of the ANOVA model and interpretation of the overall F-test

I've recently encountered the following concept regarding linear regression: the inclusion of an intercept will change the interpretation of both the overall F-test and the individual parameter ...
0
votes
1answer
17 views

Log likelihood for inverse gamma

For a gamma distribution, the answer to this question shows that you can just use the log of the gamma distribution density function. Is the same true for inverse gamma? It is the same as the log of ...
1
vote
0answers
19 views

Questions regarding interpretation of item parameters: inconsistent results between classical item analysis and item response analysis

I was not trained as a psychometrician, so my questions may be concept-related. If you know different resources, books, or articles that can clear my confusions, please let me know. Thank you very ...
1
vote
0answers
16 views

p-test for parameter

Statisticians, number magicians, I was wondering what the best way is to check for the equality of two parameters including possibly a confidence interval and p-value. $$H_0:\beta_1=\beta_2\vert\ ...
3
votes
1answer
70 views

Questions Regarding Item Response Models – the 3PL model vs. the mixture of 2PL and 3PL model

I currently work on an item analysis for an assessment. The assessment consists of 25 multiple-choice questions. Each question has 4 choices with one correct answer. I used different IRT models to ...
0
votes
1answer
31 views

OLS parameter estimation of an expression?

During my research for a class, I came across a paper that said they estimated an equation using OLS. But the parameter they were estimating appeared to be an expression that looked like this (not the ...
1
vote
0answers
18 views

Under what assumptions can parameter estimate uncertainty be estimated from the Hessian?

Given a model with some parameters, some data it's attempting to reproduce, and a distance function to quantify how well the predictions correspond to the data, I can fit parameters via a general ...
1
vote
1answer
38 views

Beta binomial mean different from actual mean

I have data that I am fitting a beta binomial distribution to. The VGAM package for R is being used to do this. However, the the mean of the fitting beta binomial distribution is vastly different from ...
0
votes
0answers
20 views

Implementing evolutionary algorithms

We're trying to to minimize the following functions using Multi Objective Evolutionary Algorithms : $\mathrm{minimize}~\lambda_q(1-a)E(N)E(X)+\lambda_xE(N)E(\max(X/a-R,0))$ ...
2
votes
1answer
31 views

Parameters in a non-parametric model

I have not understood this Wikipedia statement: The difference between parametric model and non-parametric model is that the former has a fixed number of parameters, while the latter grows the ...
2
votes
2answers
31 views

Estimation of probability mass function using finite samples [closed]

Suppose $X_1, X_2, \dots, X_N$ are $N$ random samples of a discrete probability distribution such that $X_i \in \{1, 2, \dots, K\}$. The probability distribution $p$ used for sampling is ...
0
votes
1answer
43 views

Beta distribution vs beta binomial distribution: alpha and beta

I have been attempting to estimate alpha and beta from a beta binomial distribution given my data. There are R packages like VGAM to do this. I am wondering if there is a difference between estimating ...
1
vote
1answer
17 views

Prior for gamma distribution in “mean form”

I need to specify priors for the parameters of a gamma distribution. Normally the gamma distribution is parametrized in either the "rate-form'': ...
0
votes
0answers
8 views

Redundant parameters in multilevel models

I am a bit confused about the use of redundant parameters in multilevel models in order to speed the convergence of the Gibbs sampler. I don't understand how the model should be reparametrized. ...
1
vote
1answer
13 views

Is there a family of processes centred on the Poisson process?

I am looking for a model, characterized continuously by a single parameter, to describe the arrival times of buses with unit expected interarrival time. At one extreme of the parameter (say ...
1
vote
0answers
39 views

What have I done wrong implementing this Bayesian method for fitting a circle to noisy data?

I have noisy measurements of movement along a circle. I want to fit a circle to these measurements. I tried two methods, a straight forward moment fit, and then an ODR fit (described here. However ...
2
votes
1answer
75 views

CV for model parameter tuning AND then model evaluation

I have a basic question on using cross-validation for model parameter tuning (model training) and model evaluation (testing) similar to this Model Tuning and Model Evaluation in Machine Learning I ...
8
votes
2answers
176 views

Fisher information matrix determinant for an overparameterized model

Consider a Bernoulli random variable $X\in\{0,1\}$ with parameter $\theta$ (probability of success). The likelihood function and Fisher information (a $1 \times 1$ matrix) are: $$ \begin{align} ...
3
votes
1answer
332 views

Find the mode of a probability distribution function

I am trying the find mode of a probability distribution function given by \begin{equation} g(x/\alpha,\beta,\sigma)=\frac{1}{\Gamma \left( \alpha ...
0
votes
0answers
27 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 ...
0
votes
0answers
28 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 ...
3
votes
0answers
25 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 ...
0
votes
0answers
31 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 ...
1
vote
1answer
212 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 ...
0
votes
1answer
86 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) ...
1
vote
1answer
38 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
votes
1answer
79 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 ...
1
vote
0answers
29 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?). ...
0
votes
1answer
70 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 ...
0
votes
1answer
30 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: ...
-1
votes
1answer
29 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
vote
1answer
100 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 ...
0
votes
0answers
34 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 ...
0
votes
1answer
61 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
vote
1answer
47 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
votes
1answer
131 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 ...
0
votes
1answer
28 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: ...
1
vote
0answers
58 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 ...
0
votes
0answers
35 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 ...
0
votes
2answers
117 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 ...
0
votes
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 ...
0
votes
0answers
96 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, ...
1
vote
0answers
25 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
votes
1answer
41 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?
0
votes
0answers
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 ...
1
vote
3answers
101 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
votes
0answers
224 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 ...
1
vote
1answer
205 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 ...
1
vote
1answer
514 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
votes
1answer
327 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 ...