Latent variables refer to variables that cannot be directly observed. These variable are defined in terms of observable variables.

learn more… | top users | synonyms

0
votes
0answers
25 views

Quasi-Newton Accelerator (QN1) for EM Algorithm

I am trying to implement what is called a "very simple to implement" accelerator for the EM algorithm. Specifically I am talking about the QN1 algorithm, described here, and am using a multivariate ...
0
votes
0answers
15 views

disease progression R markov chains

hello i have a dataframe, some of the columns include: remission,height,weight,time from diagnosis, age ethnicity, age, patient id remission is 1 or 0 just to be clear i want to fit an appropriate ...
1
vote
1answer
47 views

Expectation-Maximization Algorithm for Binomial

I have a multinomial distribution with four outcomes, with a pdf: $$p(x_1,x_2,x_3,x_4)=\frac{n!}{x_1!x_2!x_3!x_4!}p_1^{x_1}p_2^{x_2}p_3^{x_3}p_4^{x_4}, \sum_{i=1}^4x_i=n, \sum_{i=1}^4p_i=1$$ The ...
0
votes
0answers
14 views

Growth mixture modeling with latent variables in R with lcmm

I am trying to replicate the analysis that was used to make this figure: I have measured the depression levels (a quantitative variable) in my subjects at the following time points (in months): ...
0
votes
0answers
26 views

Should I be concerned that the correlation between factors changes sharply when I move from EFA to CFA?

I am running an EFA and CFA on the same data (I realise this would not normally be appropriate). I've found that when I do an EFA (direct oblimin rotation) with a four-factor solution there is a ...
5
votes
1answer
62 views

LDA vs word2vec

I am trying to understand what is similarity between Latent Dirichlet Allocation and word2vec for calculating word similarity. As I understand, LDA maps words to a vector of probabilities of latent ...
0
votes
0answers
16 views

linear discriminative analysis for regression

LDA computes a projection matrix to maximize class conditional probability. Similar to this, is there any exisiting method or library for jointly learning latent space and minimizing the regression ...
0
votes
0answers
22 views

Is the bias of a coin a latent variable or a parameter?

Consider the standard Bayesian estimation problem in which the bias $p$ of a coin is picked uniformly at random from $[0, 1]$, the coin is tossed a few times, and $p$ is then estimated from the ...
0
votes
0answers
27 views

Intuition for understanding latent variables

I am having difficulty understanding and proving the GMM/LDA model, for I think I do not really understand latent variables. To be more exact, I cannot understand the latent variable z_k(categorical ...
1
vote
0answers
13 views

How to label observations based on latent class analysis

I perform a latent class analysis to a dataset of binary variables with ...
1
vote
0answers
98 views

Can logistic regression be modified to predict a distribution, not just point-estimate? Other ways to learn a beta distribution from binary events?

Currently I'm using high dimensional logistic regression to predict the probability of a rare event. I use this probability for both ranking and for other calculations which need it to be ...
4
votes
1answer
23 views

Dangers of treating residuals as latent variable?

I have data on variables ($y, x_1, x_2$), a linear model with the following form: $$ y = \beta_0 + \beta_1x_1 + \beta_2x_2 $$ and a theoretical assumption that the residuals represent a latent ...
0
votes
0answers
17 views

Resolve dispute over permanently latent variable?

I have data on variables ($y, x_1, x_2$), a linear model with the following form: $$ y = \beta_0 + \beta_1x_1 + \beta_2x_2 $$ and a theoretical model that the residuals represent a latent variable ...
1
vote
0answers
29 views

How to answer skeptic who points to latent variable?

I have a linear model with the following form: $$ y = \beta_0 + \beta_1x_1 + \beta_2x_2 $$ As $y$ is considered a "moral" behavior, I expect resistance and skepticism of the following kind: the ...
0
votes
1answer
13 views

Factoring a probability distribution containing a latent variable

I distribution which involves 3 parameters, which I'll call (for now) $P(z | y, x)$. However, one of the parameters is a function of another. For instance, let the random variable $y$ be a ...
0
votes
1answer
70 views

Generating data from Probit regression, cut off 0 and variance 1 necessary?

I am trying to create a dataset using a Probit regression model in R, where I have an intercept and three covariates. I first fix a set of coefficients for the three covariates, generate these ...
0
votes
0answers
9 views

Sub-space / latent-space covariance

I am not entirely sure what I should be googling for this in my present context. Basically I am working with a set of latent variable models such as the Gaussian Processes latent variable model ...
0
votes
0answers
23 views

Estimate the conditional distribution of an latent variable?

What techniques might best illuminate the underlying conditional distribution of a latent variable and what information or assumptions would improve that illumination? For example, if we have data ...
2
votes
1answer
135 views

Selecting the number of mixtures / hidden states / latent variables

My question is regarding Gaussian Mixture models, Hidden Markov models (HMM) or any type of clustering or latent variable model, for which we can devise a likelihood function. Specifically, I train a ...
6
votes
1answer
97 views

Parameters vs latent variables

I have asked about this before and have really been struggling with identifying what makes a model parameter and what makes it a latent variable. So looking at various threads on this topic on this ...
0
votes
1answer
32 views

Universal approximation of probability distribution with latent variable model

I want to show that under certain circumstances this form can approximate any probability distribution. For that, I came up with the following argument. Consider a directed graphical model of the ...
0
votes
1answer
25 views

Structural Equation Model - Construct Operationalization

I'm building a Structural Equation Model and I'm trying to operationalize my Dependent Variable, the construct of "Investor Behaviour" (= whether or not an investor is willing to invest in a startup). ...
0
votes
0answers
16 views

Extracting latent factor from ranking data

I want to construct a model to link between a set of quantitative engineering parameters (e.g. Sm, Ra) of 9 material samples to a set subjective qualitative rankings of 6 perceived attributes, e.g. ...
0
votes
0answers
15 views

Suggestions of a statistical model with IID data and latent variables

While this might be an unusual request, I am looking for a statistical model with certain properties to test my numerical method on and thought I might ask here. The model ought to have the following ...
-1
votes
1answer
25 views

How can I infer the joint distribution of an observed and a latent variable?

I have a dataset of school children with three features: Age $x$ of the student answering the survey Year group $a$ of the student answering the survey Year group $b$ of the best friend of the ...
3
votes
1answer
84 views

When a CFA model has a “covariance matrix was not positive definite” problem, is it due to the dataset or the model?

I am testing several CFA measurement models with Lavaan in R. The questionnaire that I am investigating has been shown to be composed of 1-factor, 3-factor, and 4-factor. In the dataset, I found ...
3
votes
1answer
180 views

Combining several variables into one outcome score: How is it done in the machine learning community?

I have got 8 cognitive (continuous) behaviour variables and would like to combine them into a composite score. I would then like to find the best predictors of this outcome (from about 50 predictors). ...
1
vote
1answer
56 views

Easy and common way to separate latent classes from Likert-scale survey?

Let say, there are two 5-points Likert-scale questions "How do you like a flavor?" applied for two product samples. That is, each respondent tries two samples and evaluate them on Likert scale. ...
1
vote
1answer
61 views

Mean field variational inference

In Chris Bishop PRML book p.465 equation 10.6, the derivation doesn't explain why exactly the term $\int q_j ln(q_j) dz_j $ was generated, is not that term supposed to be multiplied by constant, did ...
0
votes
0answers
16 views

How do you correctly scale a formative latent variables in a structural equation model?

How does one scale a formative latent variable? I know that one option is to multiply one indicator by 1. What I do not understand is that if depending on which indicator is multiplied the direction ...
1
vote
0answers
29 views

Comparing a model with a latent variable to one without

I would like to test whether my 3 dependent variables all load onto an underlying latent variable or if a latent variable is not necessary to explain the relationship between the IVs and the DVs. In ...
1
vote
1answer
43 views

Explain: Latent Variable (e.g for Latent Dirichlet Allocation)

I am trying to understand the Latent Dirichlet Allocation but therefore I need the basic understanding about what exactly a latent variable is in that sense. I know that the basic idea of a latent ...
0
votes
0answers
30 views

Sparse PLS: algorithm for variable selection and model fitting

In the spls package in R (based on the manuscript by Chun and Keleş [1]), there is a separate specification for the variable selection and fitting in the main function, ...
0
votes
0answers
13 views

Should the rivals models include the same number of observed variables?

The question is about comparing models that include different number of observed variables. For example consider I have an 80-items questionnaire and I want to do confirmatory factor analysis (CFA) in ...
5
votes
3answers
683 views

Latent Class Analysis vs. Cluster Analysis - differences in inferences?

What are the differences in inferences that can be made from a latent class analysis (LCA) versus a cluster analysis? Is it correct that a LCA assumes an underlying latent variable that gives rise to ...
1
vote
1answer
26 views

Confusing definition of Potts model

I came across some Markov random field models and noticed something that didn't make sense to me. One of these models for a set of latent variables $\{z_{i}\}$ is the following: $$p(z) = ...
0
votes
0answers
31 views

Ways to express the relationship between a latent variable and observables

How do I express concisely the idea that the values of a number of observables is determined stochastically by the value of a latent variable? Can I say: The value of the latent variable is ...
0
votes
0answers
31 views

Conceptual links and differences between generalized and latent models

Please bear with me, as I'm trying to better understand these aspects. My understanding is that (finite) mixture models (MM) are characterized by a presence of a number of sub-populations in a ...
1
vote
0answers
34 views

Mixture distribution fitting for latent variable analysis

Are there any analytic approaches to using mixture distribution fitting for latent variable analysis? I'm specifically interested in existing approaches to determining whether mixture components ...
2
votes
1answer
62 views

What is the role/purpose of hidden variables in graphical models?

Is there a formal treatment of the role/power of latent/hidden variables in graphical models and other machine learning models (e.g., structural equation models)? For example, the Restricted Boltzman ...
0
votes
1answer
48 views

identifying latent variables in this model

I have been trying to understand EM and I am having a hard time understanding what a latent variable is. In particular, I am having issues in identifying whether in a particular model that I am using, ...
4
votes
2answers
254 views

How to choose an optimal number of latent factors in non-negative matrix factorization?

Given a matrix $\mathbf V^{m \times n}$, Non-negative Matrix Factorization (NMF) finds two non-negative matrices $\mathbf W^{m \times k}$ and $\mathbf H^{k \times n}$ (i.e. with all elements $\ge 0$) ...
0
votes
0answers
22 views

How to model occurrence counts in groups with latent membership?

I have a dataset that describes the daily history of occurrences of a certain phenomenon P among a certain population. (These are subdivided into certain classes or forms that the phenomenon can take: ...
2
votes
1answer
104 views

How to use factors (from CFA) as independent variable in Regression Analysis

I calculated 4 factors as latent constructs in a Confirmatory Factor Analysis (I use AMOS). Now I am wondering if it is possible to extract some kind of a factor score like I know it from Exploratory ...
1
vote
2answers
183 views

Binary version of Probabilistic Matrix Factorization in pymc?

I'm a newby in statistics, this is my first post, sorry for any possible mistake. There is a good Bayesian Probabilistic Matrix Factorization model introduced in: Bayesian Probabilistic Matrix ...
0
votes
0answers
22 views

Mixture model for dependent observations with additive group-level confounders

I'm looking for a special type of mixture model (described below) and I'm hoping to get some hints with regards to relevant literature to look at or names to be searching for. On the general level, ...
0
votes
0answers
66 views

Estimation parameters for latent (unobserved) variable

Here is my problem: I have 3 variables $X,Y,Z$ : $X$ is the number of clicks we observed on an web advertisement; $Y$ is the number of time a customer do a sign-up on the website after clicking ...
0
votes
2answers
136 views

Partial Least Squares structural equation modeling

Im calculating a Structural Equation model with Partial Least Squares (with R). Lets say a simple example: two Response values (R1, R2) are combined to a latent variable RespLV = weight1*R1 + ...
0
votes
0answers
29 views

How to compute a latent variable (as a composite index)

Can I calculate a latent variable (construct) after the formula: Sum of (summated scale for factor i * variance explained by factor i), i taking values from 1 to n (n being the number of factors)? Or ...
0
votes
0answers
46 views

Summated scales for factors (latent variables)

For summated scales: can I use all the variables that load on a factor or is it necessary to use only the variables with high loading (over 0.70, for example)?