Questions tagged [multinomial-probit]

The multinomial probit is an extension of the probit regression. The dependent variable can take on 3 or more unordered values. The modell does not have to fulfill the IIA Assumption.

Filter by
Sorted by
Tagged with
0 votes
0 answers
11 views

Multinomial analysis in SPSS, results came with warnings about Hessen matrix

I performed a multinomial analysis in SPSS with 5 nominal dependent variables and 18 ordinal independent variables but keep getting this warning: Unexpected singularities in the Hessian Matrix are ...
user avatar
2 votes
0 answers
64 views

Multinomial probit: can covariance of coefficients be calculated from predicted probabilities?

When producing a GLM (generalized linear model), one usually wants to have an estimate of the variance-covariance matrix of the fitted coefficients, which happens to have a closed form solution with a ...
user avatar
0 votes
0 answers
26 views

Alternative specific constants in willingness-to-pay estimates

I am estimating a set of discrete choice models (multinomial logit, mixed logit, multinomial probit) models in order to compute willingness-to-pay (WTP) for attributes in included in the model. The ...
user avatar
  • 11
2 votes
1 answer
377 views

Number of features in multiclass Logistic Regression with categorical predictor

Assume that I want to predict a response with 3 classes. I have two features $X_1$ and $X_2$ where $X_1$ is continuous and $X_2$ is categorical with 5 categories. What would be the number of ...
user avatar
1 vote
0 answers
82 views

Identification of correlated errors with multinominal probit

Consider the multinational probit model where we observe $Y_i \in \{1, \dots, K + l\}$ with $$ \begin{align*} Y_i = l \Leftrightarrow Z_l&\geq \max(Z_1,\dots Z_{K +1}\} \qquad l \in \{1, \dots, ...
user avatar
4 votes
2 answers
76 views

When and why should psychologists use ordered probit models rather than the general linear model?

Psychologists often use the general linear model with ordinal independent/dependent variables (i.e. Likert scales to measure 'levels' of a psychological trait. For example, assigning numbers to the ...
user avatar
2 votes
0 answers
312 views

Heckman selection model: probit selection & logit outcome

I have a situation where I think I need to use a Heckman selection model to correct for endogeneity. I am interested in studying the effect of firm's market entry mode on its performance. Factors that ...
user avatar
  • 171
1 vote
1 answer
22 views

Obtaining posterior outcome probabilities Multinomial Probit given posterior parameter estimates

I'm trying to compute the individual posterior probabilities from my estimated multinomial probit model. I have obtained the latent utilities as well as the posterior parameters for beta and sigma for ...
user avatar
2 votes
1 answer
3k views

Conditional Logistic Regression in R

As my first question addressing this matter was incomplete and unclear, I made another attempt with an improved outline. I am currently working on a project in which I have a data-set of the following ...
user avatar
  • 108
4 votes
1 answer
179 views

Predicted Probabilities in Multinomial Probit Model

I am trying to compute predicted probabilities from a multinomial probit model. Researching the internet has given me two ways how this can be done. The first one is basically just applying the ...
user avatar
  • 652
1 vote
2 answers
1k views

Poisson or Multinomial Logistic Regression (or something else)?

There seems to be a lot of discussion about this on CV but none quite answer my question. I have a variable y which represents number of adverse events occurring ...
user avatar
  • 1,451
2 votes
1 answer
405 views

Estimation of Demand and Substitution in Case of Multinomial Logit Model

In MNL model, given the different alternative choices, we estimate the probability of choice across different options. E.g. using the fishing data, we can use the following calculations to estimate ...
user avatar
  • 5,844
1 vote
0 answers
166 views

What is an intuitive way to understand an ROC curve for a multinomial classifier?

When drawing an ROC curve for a binary classifier, we vary, say the probability threshold of one class vs the other and get the curve. However I'm confused what this means in a multinomial case where ...
user avatar
  • 1,525
3 votes
2 answers
1k views

How to use multinomial probit coefficients to predict?

I fitted a multinomial probit model with one independent categorical variable Y (levels 1,2,3) and two explanatory variables X1 and X2. Using mlogit package in R like this: ...
user avatar
2 votes
0 answers
249 views

Multivariate multinomial probit

I would like to jointly estimate 4 variables. Two of them are categorical and the two others are binary. So I thought about a "multivariate multinomial probit model", but did not find much. What ...
user avatar
  • 21
1 vote
1 answer
50 views

Is a win-lose model for three Presidential primary candidates appropriate? Multinomial Logit model?

I have a dataset where each record represents a collection of variables for each of the counties in New York. Five variables represent the number of tweets in that geographic area for each candidate ...
user avatar
  • 121
0 votes
0 answers
158 views

Discrete choice models; estimating probability of new alternatives

Let's say the set of alternatives are $j=1,2,...J$, where $X_i$ is the vector of subject-specific attributes for subject $i$, and $Z_{ij}$ is the vector of alternative-specific attributes for ...
user avatar
1 vote
0 answers
36 views

Deciding between Probit and Multi-nominal Probit if one of three categories is very unlikely? Proving randomness of that category?

I have a choice variable, which is either A or B or C. But apparently C is hardly chosen. And for me it seems like it more "happens to be chosen". I want to estimate which some continuous covariates, ...
user avatar
1 vote
0 answers
266 views

Solution verification - calculation of second derivatives of multinomial probit log-likelihood function

The initial function of log-likelihood of multinomial probit model with $J$ alternatives: $ ln \ell=\sum_{i=1}^N\sum_{j=1}^{J-1} y_{ij} \cdot ln \Phi(\sum_{k=1}^Kx_{ik}\beta_{kj})+ ({n_i-\sum_{j=1}^{...
user avatar
  • 673
4 votes
1 answer
10k views

Difference between multinomial logit and multinomial probit

Similarly to the question Difference between logit and probit models I am wondering what is the difference between a multinomial logit and a multinomial probit. And when should I apply which of the ...
user avatar
  • 4,912