How should I handle a mass-point in the dependent variable when running OLS regression in R? I’m working with a a household expenditure dataset (Living Costs 2019) where the dependent variable is the ...

I am trying to generate an ARIMA process with statsmodels. I tried already different combinations but nothing works. There is also nothing in the documentation that could solve my problem. The ...

I am trying to perform a Monte-Carlo simulation on quantile regression using R. Currently I am getting stuck simulating the data from the model below. Y=beta_0+beta_1*X1*u1+u2 where, u2~N(0,1),u1~exp(...

I want to use deming regression for the calculation of a linear function between two variables which both have measurement errors. In addition, I have to assume that the regression goes through the ...

I would like to conduct a F-test (Wilks' Lambda test, ideally) to test the equality of the slope coefficients for a single independent variable on two dependent variables. Using the mtcars dataset, ...

I would like to perform a Deming regression through the origin including a ratio of variance of 2.1 between the x and y variable, as the data I am working with includes measurement errors in the x and ...

I'm performing Gaussian process regression using GPyTorch. I'm modeling two correlated tasks as follows: class MyModel(gpytorch.models.ExactGP): def __init__(self, X, Y, likelihood): super(...

I'm potentially interested in performing ODR for some laboratory data, so I have started to play around with scipy.odr using fake data, just for the sake of learning. In this fake data example, ...

I have a dataset with a binary outcome income and two continuous predictors, age, and education_num. I'm fitting a logistic regression model with a natural spline for age and an interaction with ...

I've implemented standard homoskedastic multitask Gaussian process regression using GPyTorch as follows: class MyModel(gpytorch.models.ExactGP): def __init__(self, X, Y, likelihood): super(...

I am analyzing competing risks data in R and want to confirm that I’m setting up both Fine-Gray and cause-specific Cox regression correctly. My dataset encodes the event status as: status = 1: ...

When you estimate a model, the estimation function will drop observations (i.e., rows) for which at least one variable (i.e., column) used either in the LHS or in the RHS of the formula is missing. ...

I encounter error: Application terminated with error: ??+0 (0x709D9F003D8A) ??+0 (0x709D9E884BA4) ??+0 (0x709D9E9F388C) ??+0 (0x709D9FF2FCF5) ??+0 (0x709D9FF31448) ??+0 (0x709D9EB84E21) ??+0 (...

I'm following this to get proportional odds regression for a likert scale but I'm getting this error when I try to use predict: Error in X %*% object$coefficients : non-conformable arguments This is ...

Sample Code library(quantreg) df <- data.frame( outcome = c(10, 12, 14, 11, 13, 15, 9, 8, 10), group = factor(c("A", "A", "A", "B", "B", "...