Questions tagged [identifiability]

A model is identifiable if a single set of parameters can be found that will yield the best fit.

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Model identifiability in SEM

I am trying to fit a model with a structure similar to others already published (see Nees et al., 2012 Neuropsychopharmacology). In particular, the model structure is organized in 3 latent variables (...
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Gaussian Processes and Identification/Identifiability Issues

I'm looking for references of Gaussian Processes and identification issues that may occur. For example, in Kennedy and O'Hagan's (2001) Bayesian Calibration of Computer Models, we have $$y_i=\eta(...
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Identification of discrete choice models

Consider the classical Logit model. In particular, let $\mathcal{Y}\equiv (0,1,...,L)$ be the set of options available to consumers, where $0$ denotes the outside option. Let $$ u_y\equiv \begin{...
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Fixed Effects: Group level variables but individual level outcomes

tl;dr: In fixed effects and first difference estimation, does having sets of individuals where the change in $X_{it}$ over time is identical lead to estimation problems? When using fixed effects (FE) ...
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Identifiability of parameters in a linear model when covariates are random

Suppose we have a linear model (in $\mathbb{R}^n$, say), $$y = X\beta + \epsilon $$ where $\bf{\epsilon}$ is Gaussian with mean $0$ and covariance matrix $\Sigma(\theta)$ where $\theta$ is an unknown ...
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Why is model unidentifiability a problem?

I am new to the concept of model identifiability, but from my understanding it is possible to learn the true parameter values of the model after obtaining an infinite number of observations from it. ...
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132 views

What does the sum to zero constraint mean?

In an ANOVA model, there is a constraint that the coefficients must sum to zero. What does this actually mean? I do understand the reason why you might want to make them sum to zero, i.e. to have two ...
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What does it mean to “non-parametrically” identify a causal effect within the super-population perspective in causal inference?

I am wondering, within the context of causal inference, what it means to "non-parametrically" identify a causal effect within the super-population perspective. For example, in Hernan/Robins Causal ...
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GLMM in R doesn't converge, nearly unidentifiable [duplicate]

I'm building my GLMM using r. ...
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1answer
99 views

What does it mean if the Average Treatment Effect (ATE) in causal inference is not identifiable?

I read from the following slides on observational studies, pg. 16, Observational Studies, Keio, that given: $$ ATE ≡ E[Y_i(1) − Y_i(0)] $$ They pose the following question: Can we identify the $ATE$...
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What is “identification assumptions” in econometrics? [closed]

I'm starting to study econometrics from Wooldridge's book. But some doubts arise regarding to the role of Conditional Expectations in Econometrics. Wooldridge says that although it is not always ...
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Error variance may not be identified. LISREL

I am conducting CFA for a variable in my study. It has two sub-dimensions, say D1 and D2. On conducting CFA with only first-order factors, my model runs fine and there is a strong correlation among ...
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Can anyone help explain this basic example of posterior

I am having trouble understanding the authors reasoning here. It is from "The Bayesian Choice" I am confused about why the posterior is initially written without depending on the data, and why we ...
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Alternatives to calculating the rank of the information matrix in determining if the model is identifiable

I have a known non-linear model $h \in \mathbf{R}^n$: $$ y = h(\theta) + \epsilon, $$ where $\theta\in \mathbf{R}^m$ is a parameter vector, and $\epsilon$ is a normal random variable with zero mean ...
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VECM with Multicollinearity

I have fit a vector error correction model (VECM) to some macroeconomic data. In particular, I am interested in three relationships real GDP as a function of employment and real wages employment as a ...
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Uniqueness on bayesian factor model's loading matrix

I'm doing uniqueness on factor loading matrix in a factor model. $ y = \Lambda f + \epsilon$ where $ f \sim N(0,\Sigma)$ , $\epsilon \sim N(0,\Omega) $ and $\epsilon \perp f$. It's well known that ...
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Reference smooth + smooth by all levels of a factor: is my GAM still identifiable?

I have speech signals sampled in 10-ms intervals ('$time$') in 8 different geographical regions ('$region$'), from 20 subjects each. For each of these regions, I want to know if the sampled trajectory ...
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Definition of softmax function

This question follows up on stats.stackexchange.com/q/233658 The logistic regression model for classes {0, 1} is $$ \mathbb{P} (y = 1 \;|\; x) = \frac{\exp(w^T x)}{1 + \exp(w^T x)} \\ \mathbb{P} (y =...
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Is this model identified?

Is this model identified? The paper is in plosone, but it seems to be that the combination of regression paths from PCS ~ MCS and ...
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1answer
764 views

just-identified model and over-identified model in the context of instrumental variable

According to this pdf, when number of instrument variable equals to the number of endogenous components, the model is said to be just-identified; if number of instrument variable is bigger than the ...
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Can every identifiable model be estimated by GMM?

Assume a model with parameters $\theta$ is identifiable. Then that means that for every probability distribution over observable variables $p(x|\theta)$, there is a unique parameter value $\theta$. ...
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What is a “weakly identified” parameterization?

I understand that a parameterization is identified if it's true that $$ \theta_1 \neq \theta_2 \Rightarrow p(y|\theta_1) \neq p(y|\theta_2) $$ Intuitively, it means that two different parameter ...
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Assumptions of MLE

I am currently reading up on Maximum Likelihood Estimation in Studies in Econometric Method. When describing the requirements for MLE to be consistent, they described it as the following: A number ...
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A test of my understanding of identification

I've been asking a few questions about identification lately, so forgive me for another one: Throughout this question, let $\mathbb P$ denote the set of probability distributions consistent with the ...
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When a prior distribution would not be overwhelmed by data, regardless of the sample size?

I came across a question 8 at the end of chapter 3 of the book: "Give two simple examples showing a case in which a prior distribution would not be overwhelmed by data, regardless of the sample size"...
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Estimate a parameter from subset of the data, other parameters from all data

I use Bayesian random effects models [$y_i \sim bernoulli\_logit(\beta + \alpha_{subj})$ $\alpha_{subj} \sim normal(0, \gamma)$], the $y$ outcome is binary. Part of the subjects have two observations,...
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285 views

Linear regression model identifiable?

I understand the concept of identifiability in the context of distributions. This is $f$ is identifiable if $f(x;\theta) = f(x;\theta')$ for all $x$, if and only if $\theta=\theta'$. However, in the ...
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Identifiability of neural network models

It's quite intuitive that most neural network topologies/architectures are not identifiable. But what are some well-known results in the field? Are there simple conditions which allow/prevent ...
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What's wrong with this argument that the parameter $\beta$ is always identified in the linear regression model?

If we have a linear regression model $y=X\beta + e$, then $E(y)=E(X\beta)+E(e)=E(X)\beta + 0$ Therefore $$E(X)\beta = E(y)$$ Doesn't this pinpoint the value of $\beta$, assuming that the sample size $...
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State Space model identification with Kalman Filter [duplicate]

If I have a standard state-space model where all parameters are unknown (coefficients and covariance matrices for both the state equation and observation equation) and I want to estimate it with the ...
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How to empirically identify trends exactly offseting each other?

Say a random variable $Y$ is affected by both $X$ and $Z$, in the following way (data generation process): $$ y_t = a + bx_t + cz_t + \epsilon_t $$ where $\epsilon_t$ is a white noise. Furthermore,...
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In Bayesian estimation, when can regression coefficients and scale parameter be jointly identifiable? When not?

Exercise 14.2 in Koop, Poirier and Tobias's book (i.e. Bayesian econometric methods) talks about the case that in probit model, the regression and scale parameter are not jointly identified. I ...
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275 views

Is the following model parameterization identifiable?

Let $X_i's$ be independent $i=1,2...n$ with $X_i\sim N(\mu+\alpha_i, \sigma^2)$ for each $i$. Let $\theta=(\alpha_1,...\alpha_p,\mu,\sigma)$ and $P_\theta$ be the joint pdf of the $X_i's$. So, $P_\...
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Verifying model identifiability via simulations

I'm working on a nonlinear mixed effects model with 4 parameters. I've simulated some data to fit my model to, and I would like to check that the model is identifiable. I'm trying to outline some ...
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191 views

Identifiability of Gaussian process parameters

The Gaussian process model (GP) is written as $$y(x)=h(x)^{t}\boldsymbol\beta + f(x)+\epsilon(x)\qquad[1]$$ where $h(x)^{t}$ is a regression function such as $\left [1,x,x^{2} \right ]$ ; $\...
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308 views

Identifiability of Poisson parameters

Assume that you have two Poisson random variables, $y_{jk} ∼ Poi(\lambda_{jk} \psi_j)$ and $y_{kj}∼Poi(\lambda_{jk}\psi_k)$. I've read that this parameterization is not unique, but for me it is not ...
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How can we see that this model is identified?

Let the density function be given by $$ f(x;a,b) = \frac{a + 2 b g(x) + (1-a-b) g(x)^2}{(1-x)(2 b g(x) + (1-a-b) g(x)^2)}$$ where $a$ and $b$ are parameters of interest and $g(x)$ is a known ...
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Get different results with different sampling order in Gibbs sampling: what could be wrong?

In sampling a complex spatio-temporal model by Gibbs sampling, I found if I change the order of sampling (for example, to sample $P(\theta_1,\theta_2|D)$, in one try, I sample $\theta_1\sim P(\theta_1|...
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Stochastic volatility model - why not identified?

For the following model $y_t = \beta e^{h_t/2} \epsilon_t$ $h_{t+1} = \mu + \phi(h_t - \mu) + \sigma_n \eta_t$ this Kim et al (1998) paper writes that For identifiability reasons either $\beta$ ...
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Identifiability in a nonlinear regression problem

Suppose I'm working with the following model $y_i = \alpha(1-\exp(-\beta t_i))+\gamma(1-\exp(-\delta t_i)) + \varepsilon_i$. The $\varepsilon_i$ are i.i.d. gaussian with zero mean and I'm trying to ...
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2k views

Sum-to-zero constraint in one-way ANOVA

I'm trying to understand my lecture notes but am a bit stuck on the concept of identifiability. In one-way ANOVA, could someone please explain the reason for the constraint $\sum_{i=1}^{m} \beta_{j} = ...
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156 views

Fitting least squares when number of predictors are larger than instances

A statement from the book Introduction to Statistical learning with applications in R, didn't quite make sense to me. It says, "In cases when number of predictors are greater than the instances we ...
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1answer
101 views

Identifiability Versus Convexity

I'm a little unclear on the definitions of "identifiable" and "convex." Consider the case where $X_1, \ldots, X_n \overset{iid}{\sim} \text{Bernoulli}(p)$. Then our likelihood function is $L(p) = p^{\...
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Is there a term for dividing out a parameter to make model identifiable?

Suppose I have a nonlinear model of the form: \begin{align}EY|X = \frac{aX}{aX+b}\end{align} where $a, X, b > 0$. I reparameterize the model as \begin{align} EY|X =\frac{\beta X}{1 + \beta X} \...
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Choice specific variables in multinomial logistic regression

For a MNL regression, is it possible to include independent variables that are only populated for one of the choices? Let's say there are 3 choices an individual has. However, only choice 3 has an ...
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Identifying the parameters of a linear state-space-model using Kalman Filter

I have a linear state space model (SSM) that looks like this \begin{align} {\dot {x}} & = {\rm \textbf{A}}{x} + {\rm \textbf{B}}{u} \\ {y} & = {\...
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How to check if the parameter of a statistical model is identified?

I understand the definition of identifiability but I'm not sure how to check for it. Could I just take the expectation of the distribution with two different parameters and show they are different in ...
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Linear moment inequality for layman

Can someone please explain Manski's approach to Partial Identification of Probability Distributions in very basic terms? I have gone through a lot of stuff but failed to find an intuitive explanation. ...
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1answer
175 views

Identifiability of ordered regression cutpoints

I have an ordered regression model as described in ?polr: The ordered factor which is observed is which bin Y_i falls into with breakpoints zeta_0 = -Inf &...
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213 views

Alternative construction of ARMA(1,1) process

My question is related to the exercise 2.9, p. 79 in Brockwell & Davis, An Introduction to Time Series Analysis and Forecasting, 2nd edition, New-York, Springer, 2002 (It is also related to ...