Questions tagged [parameterization]

For questions about how to parameterize some statistical model, or comparisons between different ways to parameterize.

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39
votes
7answers
6k views

Would a Bayesian admit that there is one fixed parameter value?

In Bayesian data analysis, parameters are treated as random variables. This stems from the Bayesian subjective conceptualization of probability. But do Bayesians theoretically acknowledge that there ...
31
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1answer
6k 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 ...
19
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5answers
2k views

What's in a name: hyperparameters

So in a normal distribution, we have two parameters: mean $\mu$ and variance $\sigma^2$. In the book Pattern Recognition and Machine Learning, there suddenly appears a hyperparameter $\lambda$ in the ...
12
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2answers
3k 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 ...
11
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2answers
12k 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) ...
10
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0answers
388 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, I'...
9
votes
2answers
1k 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} \...
9
votes
1answer
185 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 ...
9
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1answer
359 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 ...
8
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1answer
3k views

Can I get the parameters of a lognormal distribution from the sample mean & median?

I have the mean and median values for a sample drawn from a lognormal distribution. Note that this is not the mean and median of the logs of the variable, though I can of course calculate the logs of ...
8
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1answer
1k views

What is the point of graphical models?

I spent the day learning about the bnlearn package in R only to discover that Bayesian models do not work with undirected graphs. I'm trying to learn about the Markov Random Field Network, and so far ...
8
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2answers
3k 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 ...
7
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1answer
8k views

What does the cost (C) parameter mean in SVM?

I am trying to fit a SVM to my data. My dataset contains 3 classes and I am performing 10 fold cross validation (in LibSVM): ...
7
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2answers
7k 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 ...
7
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3answers
358 views

GLMs must be 'linear in the parameters'

I am experiencing some cognitive dissonance about what 'linear in the parameters' means. For example, here and here. For example, my understanding is $y_i = \beta_0 + \beta_1\beta_2x_1 + \exp(\beta_3)...
7
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1answer
2k 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 ...
7
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1answer
3k 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 ...
6
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1answer
1k 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 ...
6
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1answer
237 views

Generalized Normal Distribution

Is there a known distribution, $f(x|\theta_1,\theta_2,\theta_3,\theta_4)$, with the following properties: $E(X^n)=\theta_n$ for $n \in \{1, 2, 3, 4\}$. If $\theta_3=0$ and $\theta_4=3\theta_2^2$, ...
6
votes
1answer
14k 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 ...
5
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2answers
4k views

Scale parameters — How do they work, why are they sometimes dropped?

I'm having difficulty wrapping my head around scale parameters. How exactly do they work? Why are they sometimes ignored? (in other words, when is it important to preserve them in a calculation?) ...
5
votes
1answer
4k views

Algorithms for weighted maximum likelihood parameter estimation

What are the computational or algorithmic considerations for weighted maximum likelihood parameter estimation? That is, I want to get $$ \theta^* = \arg\max\limits_\theta \sum_i w_i \log(\mathcal{L}(\...
5
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2answers
4k views

How to interpret slope parameter estimates for linear models in R?

I wish to analyse a simple lab experiment. I have 8 fish. Four are fed on diet A, and four on diet B. I measure their Nitrogen (N) over 5 time periods (so 5 repeated measures per fish). I wish to know ...
5
votes
1answer
151 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 ...
5
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2answers
384 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 ...
4
votes
1answer
6k 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 \right)\beta^{\alpha}}exp\left\{{-\frac{x^2}{2\sigma^{...
4
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2answers
3k 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 ...
4
votes
2answers
2k 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 ...
4
votes
1answer
1k 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 ...
4
votes
1answer
265 views

Correct understanding of De Finetti`s representation theorem

I am currently interestend in understanding De Finetti`s representation theorem. As I am only familiar with Frequentist thinking I have some problems to understand its meaning. I have already read the ...
4
votes
1answer
782 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 ...
3
votes
1answer
2k 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 (...
3
votes
3answers
130 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 ...
3
votes
1answer
590 views

Fitting t-distribution to data and deriving moments

I'm fitting the t-distribution to financial data and I know of two methods to do this using R: (A) fitdistr(mydata, "t") Output: ...
3
votes
1answer
246 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 ...
3
votes
1answer
620 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 ...
3
votes
1answer
64 views

Formula for cross-classified (a.k.a., crossed random factors) mixed effects model with interaction between two “second level” variables

I have a crossed-classified (Hox, 2010) mixed effects model—also known as crossed random factors (West, Welch, & Galecki, 2015), but I am struggling with how to write the formula for an ...
3
votes
1answer
68 views

Sampling parameters from exponential family

So suppose PDF $f_{X|\theta}(x_1,...,x_n;\theta_1,...,\theta_m)$ is from the exponential family. Is there any theory or general guidelines for sampling parameters from this PDF? This question is not ...
3
votes
1answer
117 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 = \...
3
votes
2answers
67 views

gamma parameter in xgboost

I came across one comment in an xgboost tutorial. It says "Remember that gamma brings improvement when you want to use shallow (low max_depth) trees". My understanding is that higher gamma higher ...
3
votes
1answer
63 views

good terminology for the parameters of a lognormal distribution?

Is there any good short terminology for the two parameters of a lognormal distribution? I have been using mean-log for $\mu$ and volatility for $\sigma$, where the lognormal variable $X$ has $\ln(X)$ ...
3
votes
1answer
27 views

Is there a standard name for a certain parameter for the beta distribution?

The beta distribution is $$ (\text{constant})\times x^{\alpha-1}(1-x)^{\beta-1} \, dx \quad\text{for } 0\le x\le 1. $$ Supposing $X$ to be so distributed, one has \begin{align} & \mu = \...
3
votes
1answer
208 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 ...
3
votes
0answers
85 views

Probability distribution over shapes (or: How to parameterize arbitrary polygons)

Has there been work on modeling variations of a 2D shape? E.g., say you want a distribution over 5-sided polygons, or over ellipses, or curved shapes? For simple shapes, like circles, rectangles, ...
3
votes
0answers
45 views

Family of flexible parametric mappings $f_\theta:(0,1) \rightarrow \mathbb{R}$?

For the purpose of reparameterizing a model (mostly with the goal of improving MCMC efficiency), I am looking for a family of flexible parametric mappings $f_\theta:(0,1) \rightarrow \mathbb{R}$ such ...
3
votes
0answers
56 views

How to parameterize coefficient matrix to restrict eigenvalues?

Consider the $r-$dimensional autoregression $$ y_t = Ay_{t-1} + v_t, v_t \overset{iid}{\sim}N(0,\Sigma). $$ It is well known that if all eigenvalues of $A$ have modulus less than unity then this ...
3
votes
0answers
53 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 $\min_{\...
3
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0answers
2k 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 ...
2
votes
1answer
163 views

Some questions about exponential families

Regarding the book The Bayesian Choice I understand most of chapter three on exponential families, but there are two parts I have trouble understanding. The first is Consider$$f(x|\theta)=h(x)\...
2
votes
1answer
505 views

What is the difference between the parameters and the moments of a distribution?

What is the difference between the parameters and the moments of a distribution? Are the moments of the distribution (mean, sd etc.) simply also parameters of a distribution?