Questions tagged [assumptions]
Refers to the conditions under which a statistics procedure yields valid estimates and/or inference. E.g., many statistical techniques require the assumption that the data are randomly sampled in some way. Theoretical results about estimators usually require assumptions about the data generating mechanism.
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Possible endogeneity issue with FE regression
I am doing a regression of the effects of sanctions on gdp:
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If my logistic regression model is performing well, does it matter if my features don't pass the Box Tidwell Test?
I've built a logistic regression model for binary classification with a high F1 score, but when I run Box-Tidwell tests on continuous independent features/predictive variables, I find non-linearities ...
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Unbiasness of OLS estimates under Stochastic Regressor
I found although the Gauss-Markov Theorms are so widely used, it has so many different versions. Appreciate it if anyone could help me clarify this specific question I have.
Given the OLS estimators:
$...
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The correct condition for OLS estimates to be unbiased?
For the ordinary least square (OLS) estimates of regression ($\vec{y} =\mathbf{X} \cdot \vec{\beta} + \vec{\epsilon}$) to be unbiased (without considering the efficiency), which one of the three ...
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Help to interpret the residuals vs. fitted values plot for verifying the assumptions of a linear model [duplicate]
Consider this residuals vs. fitted values plot:
Would you say this have no problems and I can go on the linear regression? I am interpreting this as heteroscedasticity and with that should not go ...
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How to Check Linearity Assumption in Logistic Regression with a Large Dataset?
I am working with a very large dataset that essentially covers the entire population of interest. I want to assess the linearity assumption between an independent variable and the log(odds) of the ...
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Robust Standard Errors as Remedy for Violation of Assumptions in Multi-Level Model
So I ran a multi-level model using the nlme/lme4 R packages. Testing the assumptions, I found that level 1 as well as level 2 residuals are not normally distributed, also there's heteroscedasticity on ...
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How to check assumptions of a binomal GLM with categorical predictors
I have a data set that looks like this (subset below):
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How to interpret the DHARMa quantile residual plot?
We calculated a GLMM based on the beta distribution:
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Multi-level regression model: assumptions and their violations
Some context:
I'm trying hypothesis testing using HLM/Multi-level regression modeling for the first time and ran into some problems testing the assumptions on my specified models.
All in all I ...
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Lavaan growth model: to treat endogenous variable as ordinal or continuous
I am modelling the trajectories of scores on two cognitive tests (i.e., PAL and SOS) measured at four time points. To do this I am creating separate latent growth curve models for each cognitive test, ...
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How should I proceed with a one-way ANOVA if homogeneity of variance is violated, I have unequal sample sizes, and I want to control for covariates?
I'm using SPSS. I have a multi-level categorical IV and a few continuous DV's.
My main analysis goals are to test the effect of the IV on the DV, and then to follow-up with pairwise comparisons of ...
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GVLMA give contradicting results on the same data compared with the inverse model
I was using GVLMA from R and this doc: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2820257/
But then I notice something weird with some models, working with the set of data, some tests of Y(X) can ...
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What to do after violation of homogeneity of regression in ANCOVA
I wanted to run an ANCOVA. My independent variable is field of study (3 groups: science, humanities, and business). My dependent variable is IQ measured on a continuous scale. There are two covariates:...
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Is identification assumption a necessary condition for causal inference?
I am confused about the identification assumptions mentioned in learning pearls causal inference book .
May I ask is identification assumption a necessary condition for causal inference?
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Testing multicollinearity assumption for logistic regression
My model is dealing with survey data for a mixed-effects logistic regression model. All of my variables in the model are categorical (most are binary). What can I use in R to assess the ...
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Correct notation when proving that $\hat{\beta_1}$ is linear
When reviewing lecture slides for the proof that $\hat{\beta_1}$ is linear in OLS-regression my teacher posted the following on the lecture slides:
$$\hat{\beta_1}=\frac{\sum(X_i-\bar{X})Y_i}{\sum(X_i-...
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Transforming Data for Linear Models
I am relatively new to statistics and need some help understanding some concepts relating to linear models and their assumptions. My question refers to one assumption, but I am using this as an ...
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Mediation analysis with a log-transformed mediator
The very basic framework for mediation analysis (as I understand it) is below (DV = dependent variable, IV = independent variable):
Step 1: DV ~ IV
Step 2: Mediator ~ IV
Step 3: DV ~ IV + Mediator – ...
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Concise listing of statistical model assumptions?
I'm still a novice when it comes to statistics, but I've noticed that, while many stats textbooks mention that the various statistical tests (t-tests, ANOVA, etc.) rest on certain assumptions about ...
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Is there any case in which evaluation of outliers does not render probabilistic sampling deterministic?
As I understand it, probability sampling means that every element in the population has a nonzero probability of being selected as part of the sample. But when some elements have a nonzero probability ...
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Difference between running a zero-truncated model on zero-truncated data and running a GLM on zero-truncated data
I apologize if this a remedial question but I am trying to determine how best to proceed with my analysis and I would appreciate if someone could explain the differences in assumptions between a zero-...
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Question regarding the assumption for Gaussian process regression error and Bayesian optimization
I am wondering in the research of Bayesian optimization and Gaussian process regression how the function error could be rigorously quantified. I am aware that in general either of the following ...
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What is a formal and authoritative definition of an 'assumption' in a statistical model?
The description of the tag in this website states that it
Refers to the conditions under which a statistics procedure yields valid estimates and/or inference. E.g., many statistical techniques ...
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Difference-in-differences regression in R: validity, assumptions, and correct usage
I am currently writing one of my first empirical papers. As a side note, the topic is the effect of a CEO change on financial performance. Conceptually, I decided on the DiD approach, as I have a ...
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Methodology for Reconciling "all models are wrong ..." with Pursuit of a "Truer" Model?
When attempting to model various phenomena, in practice, our model will usually be "wrong", but can still provide us with useful insights/predictions.
What methodologies can we follow to ...
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How to build a mixed-ANCOVA-model with lme()?
I want to perform a split-plot ANCOVA in R. I try to test, whether the assignment to one group (of two; between-subject factor) has an effect on a performance task (measured 5 times during the ...
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Is it valid to test the divergence of two timeseries using a Mann-Whitney-U test? If not, how can I show doing so will be problematic?
I have two time series that give me a monthly count. One is a reference series that is meant to be used as a control; the other has a 'treatment' or program applied to it mid-way through. Both are ...
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Why doesn't a binomial GLM successfully fix violated assumptions of simulated binomial data?
I generated binomial data showing a logistic correlation to a predictor. I analysed such dataset with a generalised linear model which assumes a binomial distribution of residuals and a "logit&...
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Where does the uncertainty of the "true" $p_{*}(y|x)$ come from?
You'll often see the goal of a statistical estimation problem as being to fit a model such that it $\approx p_{*}(y|x)$ where $p_{*}(y|x)$ is the "true distribution of the data".
My question ...
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are there any conditions on the data in ANN classification?
In regression models such as linear and multiple regression models, there are several conditions that must be met such as normality, non-autocorrelation, heteroscedasticity etc. does ANN also have ...
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How does the assumption that the distribution must be fully specified when using a Kolmogorov-Smirnov practically impact comparison of distributions?
As specified here on the NIST website, the 3rd assumption for the K-S test is "... the distribution must be fully specified. That is, if location, scale, and shape parameters are estimated from ...
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Geostatistics: Covariance vs Semivariance
I am confused by the following page in Geostatistics for
Environmental Scientists, Webster & Oliver:
My understanding
Given locations specified by a vector $\mathbf{x}$, we assume an underlying ...
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Multiple imputation and regression assumptions
I have a small data set and I have missing values and decided to use multiple imputation technique
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ARDL OLS assumptions?
can someone direct me to a paper etc. that states the assumptions of ARDL that need to hold for parameters to be valid?
ARDL is an OLS based model , does that mean the regular assumptions of Gauss-...
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Generalised Linear Model (GLM) linearity assumption checking
For GLM model, we have the linearity assumption that the transformed response variable (through) the link function) is linearly dependent on the variables. I'm checking this assumption using the ...
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Linear regression assumptions for causal Inference
When I'm using linear regression as an estimation method to infer the average treatment effect (between treatment and outcome), am I assuming there is a linear relation only between treatment and ...
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is it possible to merge multiple populations for hypothesis testing
so basically I want to test differences between two systems. I am collecting time needed for executing queries, every query was executed 50 times. I have defined some queries such as q1, q2... qn each ...
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Upward trend in Residuals vs. Fitted - Violation of linearity?
I am currently working with longitudinal data using a linear mixed model (lme4 R package).
The linearity plot (fit with ...
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Repeated measures ANOVA on proportions of errors
Statistics help please!!
I gave subjects a multiple choice test with 3 options.
There were 2 types of questions in the test (easy and hard). The non-correct answer options were either lures or non ...
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Check the homogeneity of variance assumption by residuals against fitted values
I am studying this source about One-Way ANOVA Test in R. We know that ANOVA test assumes that the data is normally distributed and the variance across groups are homogeneous. In the source the claim ...
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What is the mode of inference for frequentist IPTW estimation in the causal inference context
In Rubin 1990, Donald Rubin describes four different modes of statistical inference for causal effects:
Randomization-based tests of sharp-null hypotheses - in the tradition of Fisher, if you've got ...
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Poisson regression - How to check mean = variance assumption when the counts have different exposures?
I'm modeling rate data using Poisson regression and I want to assess the model assumption of mean = variance. I understand that for Poisson regression it's the conditional means that equal the ...
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Assumptions of GLM with negative binomial error distribution and categorical independent
I'm carrying out a study comparing the abundance of a few species between two treatment groups. I have carried out an NMS analysis to compare overall community composition and structure, but I also ...
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OLS assumptions for weighted errors
As far as I know, under satisfied assumptions for OLS, the estimates acquire qualities like BLUE, MVUE, MLE.
But in the case where there is a priori knowledge of the influence of each data point, it ...
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How to plot individual case residuals (ICRs) from lavaan model?
I was reading this article on saturated models in structural equation modeling (SEM) and it appears they describe a tool for checking how well the model fits data even if the model fit indices ...
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Dealing with violation of linear regression assumptions
I have a data set where some extreme, but not nonetheless important observations are present which prompts violation of the linear regression assumptions of normality and constant variance. The ...
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Challenges with achieving linearity in multiple linear regression analysis
What steps can I take to correct for linearity assumptions in a multiple linear regression analysis conducted in Python on a dataset with 79 independent features, including 22 continuous, 28 nominal, ...
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Can I calculate ROC AUC from Mann Whitney U tests when I am comparing unequal sample sizes?
I know I am able to calculate Mann Whitney U tests when comparing 2 samples unequal inside but I am wondering if I am able to carry this same principle when calculating ROC AUC via the formula:
AUC = ...
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ANOVA Assumptions on Big data
I wanted to know if one would require to check for the violation of ANOVA Assumptions before running an ANOVA model on a Big Dataset (size of the big dataset is 57 million rows)?
Thanks!