Questions tagged [misspecification]

Problems with model specification, such as missing variables/predictors, wrong functional form, wrong variance or covariance structure, etc.

41 questions with no upvoted or accepted answers
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103 views

When does the prediction of random effects matter?

In linear or generalized linear mixed effects models, random effects are incorporated to explain the within-unit correlation for repeated measures over time. In Bayesian modeling, conventional prior ...
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77 views

literature on small samples and parametric survival models

I have an abundance of small data sets with right-censored data. There are different groups in each data set and I'd like to get confidence intervals for the regression parameters. Each data set has 3-...
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222 views

How do I perform an actual “posterior predictive check”?

This question is the follow-up of this previous question: Bayesian inference and testable implications. For concreteness, consider the following bayesian model. This model is not to be taken ...
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76 views

Over “specification” of a statistics model?

For a simple example, I am fitting data with a likelihood function generated by a normal distribution. The first model is the normal distribution with two-parameters. The second competing model is the ...
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76 views

Does a high Chi square p-value for a whole model mean it is insignificant if likelihood ratio tests indicated variables should be added?

I've been estimating lots of versions of the same model by incrementally adding variables. With some variables, if I add them to the model, the likelihood ratio test indicates that they are ...
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5k views

What to do when ovtest and linktest in Stata suggest model misspecification?

I have a sample that consists of 50 observations. The base model of the OLS-Regression with three control variables, two of them significant, has a $R^2=0.50$ and its F-Value is 7. Both ...
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17 views

SEM: Why is there no dependable connection between the size of SEM residuals and the type or degree of model misspecification?

Kline (2016) p. 278 writes [Y]ou should know that there is actually no dependable or trustworthy connection between the size of the residuals and the type or degree of model misspecification. For ...
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28 views

Random effects for mutually inclusive grouping factors

I am trying to fit a model on a set of data (e.g. 10,000 observations with 20 explanatory variables). The observations belong to 30 groups, G1, G2, G3, ... G30, so I need to account for the grouping ...
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83 views

Relative importance different model diagnostics in LMM/GLMM

I may be over thinking things but I have been stuck on this issue for the last few days. I am currently modelling data with a large number of clusters but cluster size is limited to 2, with a skewed ...
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223 views

Maximum likelihood estimation for incorrect distribution parameter

Supposing I have a random variable $X$ with distribution $$f(x | \boldsymbol{\theta}, \boldsymbol{\varphi}).$$ Here $\boldsymbol{\theta} = (\theta_1,\theta_2,...,\theta_r)$ are $r$ parameters which ...
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58 views

How to compute the likelihood when uncertain about underlying distribution

In a Bayesian approach, when I have no previous idea about the likelihood of a given event, can I simply gather a lot of data and use that distribution as my likelihood (1) and as I gather more and ...
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569 views

Comparing Classical and Robust (Huber-White/sandwich/heteroscedasticity consistent) Standard Errors in Linear Multiple Regression

I'm running a linear multiple regression model of the type $y_i = \beta_0 + \beta_1 X_{i1} + \beta_2 X_{i2} + \beta_3 X_{i3} + u_i$. I came across King and Roberts' 2015 paper called "How Robust ...
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101 views

Is there a way to correct standard errors and/or prediction intervals for multiple comparison after doing backwards selection?

It is well known that most model selection algorithms can easily fall into a multiple comparison trap. To quote Friedman: Consider developing a regression model in a context where substantive ...
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15 views

Misspecification in the outcome model with regression adjustment for propensity score?

The propensity score is a popular tool used to control for confounding by covariates $C$ on the effect of an exposure $A$ on an outcome $Y$. There are several ways to incorporate the propensity score ...
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1answer
34 views

Question about regression using a rate vs a count as the dependent variable

Let's say I wanted to run a regression of mortality on an x variable, so I could see 3 different possibilities I can do I define a rate: $y = \left(\frac{\#deaths}{population}\right)$ and regress $$...
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18 views

How bad are short-term forecasts of ARIMA models of ARFIMA processes?

Suppose I have a set of economic time series that appear to be a unit root process. I difference them, and fit an ARMA model to the differenced series. Suppose however that the true data generating ...
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28 views
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22 views

Buld prediction intervals for glmmTMB

I'm using glmmTMB to build a mixed effect logistic model from which I want to draw predictions. The predict() function applied to a glmmTMB model allows extracting the ML prediction and the relative ...
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29 views

Bayesian inference with simple models and many data points: impact of priors and the number of data points

The setting. Let us assume I would like to perform Bayesian inference for a low-dimensional model on a large dataset, i.e., there are many more data points than there are parameters to identify. Let ...
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33 views

When regularizing based on an informative prior, how to give model a little more freedom to partially reject regulariziation

I am new here I hope this question is appropriate. I am modelling a spatial domain, whereby I have repeated measures at n locations. I make a bayesian linear model at each n locations based on about ...
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54 views

Endogenous selection bias / d-separation in SEM (Structural Equation Modeling)

I am a psychology student and currently working on a paper about omitted-variable bias in structural equation modelling (SEM). I am trying to tie endogenous selection bias to parametric SEM to ...
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17 views

What is a good test for whether a sample is drawn from a particular parametric family against a generalized alternative

Suppose I have some large number n of draws from a strictly positive distribution that I believe to be a member of a particular parametric distributional family. I use the draws to estimate the ...
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43 views

Misspecified regression & asymptotics

Consider the weighted regression coefficient $\hat{\beta} = (X'WX)^{-1}X'WY$ where the regression is possibly misspecified and $W$ is known. Let $\beta_* = (E[X'WX])^{-1}E[X'WY]$ and the related ...
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42 views

Modelling costs with correlated response variables

Say I have a data set with the following measures Number of vacation days (NumDays) Accommodation costs (Accom) Total Drinks costs (Drinks) Total Meals costs (Meals) Total Travel costs (Travel) Total ...
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33 views

Fredrick Taylor Inspired Optimization for Shovel Loads

Premise In Taylor's original Principles of Scientific Management, the optimal weight of a single shovel load was calculated (roughly 21 pounds). This had some interesting implications: short of this ...
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77 views

RESET test and AIC

What is the difference between Ramsey regression equation specification error test (RESET) test and Akaike information criteria (AIC)? From wiki, it says RESET "test whether non-linear combinations ...
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138 views

Comparing Combinations of Regressors for Linear Regression Feature Selection

I'm trying to make a time series linear regression for forecasting purposes. I have picked eight features that logically are tied to the independent variable (separate time series data sets). I ...
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141 views

Marginal standardization with robust error estimation for estimation of risk ratios

I've been using software to calculate multivariate risk ratios from logistic regression models. The basic premise is to predict the risk in everybody with some exposure and then again with the ...
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19 views

A Game Plan for Cross-Section Modelling

Imagine we have already built our linear regression model, with a certain dataset. Which order of tests would you follow to be sure that whatever conclusions you may want to extract are correct? For ...
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121 views

Parametric cdf or empirical distribution function?

Say I have a set of i.i.d. data and want to learn about the data generating distribution (the cdf would suffice). I could then fit various different suitable parametric models via say maximum ...
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1k views

Probability of Default

I'm doing a project to predict probability of delinquent for individual loans. Seems the model I fit is not good and I want to improve the model. However, I'm confused by the results I got and don't ...
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54 views

ARIMA loss of effectiveness due to non-Gaussianity

I am looking for references regarding to loss of effectiveness of ARIMA due to different misspecification. For instance the following article: Chung Chen and George C. Tiao Journal of Business & ...
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24 views

Bayes estimates and model misspecification

Consider a misspecified model: $$P \sim \text{SegmentedUniform}(0,1) \\ Y \mid P \sim \text{Binomial}(N,P).$$ Where SegmentedUniform has uniform density on intervals $$(0.1, 0.2), (0.3, 0.4), (0.5, 0....
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8 views

Variable significance very sensitive to specification of non-correlated second variable

I´m doing research on a political science topic and my models leave me behind with a big questionmark at this point. I have a dataset containing 79 observations on a number of variables and trying and ...
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1answer
30 views

Role of misspecification by biased data in the generalization error

I am confused with the role that model misspecification plays in the generalization error, in particular when the misspecification is due to a biased (non representative) training dataset. To clarify ...
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29 views

Bayesian model averaging when none of the models is well specified

Usually, from what I've read of Bayesian Model Averaging (BMA), a typical assumption is that one of the models is well specified... However, what will happen when all models are misspecified? What ...
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11 views

How does misspecification of a structural equation model impact the results or interpretation of the model?

Any ideas? I am new to SEM and am struggling to articulate why and how misspecification of the model affects results and interpretation of the SEM.
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90 views

how to interpret the smoothed effect of time in GAMM models

I have recently taken a plunge into the world of Generalized Additive Mixed Models (GAMMs) after learning that including the quadratic and cubic fixed and random effects of time to understand students'...
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1answer
61 views

Finding SSE when ignoring a parameter in estimation

The model is $Y=XB+Zy+e$ where $B$ and $y$ are unknown parameters and $e\sim \text{N}(0,σ^2 I)$. Using ordinary least squares and ignoring $Z$ in parameter estimation, I need to find the distribution ...
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89 views

How to solve a well fitted model - Model Misspecification

I am currently writing a paper, analysing the impact of goldprice movements on the capital structure of gold mining firms. My basic model is a simple OLS model with (y=leverage and x=ln(goldprice)). ...
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2k views

regression of a level vs a change variable

This question has likely been asked already but due to the lack of proper terminology I might not have been able to find google up the relevant questions. We have data for several years: 2000, 2001, ....