Questions tagged [nonlinear-regression]
Use this tag only for regression models in which the response is a nonlinear function of the parameters. Do not use this tag for nonlinear data transformation; use [data-transformation] for that instead.
1,200
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When to choose GAM over GLMM and how to include random effect into GAM
This is a follow-up question to this question.
Here is a description of the dataset: the outcome variable is the number of contacts per participant for two periods before lockdown and under lockdown. ...
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Preprocessing data for regression: scale/normalize only joint observations, or regressor and regressand observations separately?
Suppose that you observe two variables $X, Y$ (regressor and regressand) that are statistically associated, $Y \sim X$.
Your data are iid samples $\mathcal{D}:=\{(x_j, y_j) \mid j=1,\ldots, N\}\subset ...
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Is it possible to reuse predictor fixed parameters in a nonlinear mixed effects model fit across mulitple nonlinear response parameters using nlme?
I have data where I want to fit a model given that I know the value at time zero of one stage is equal to the asymptotic value of the previous stage. In particular, I have kinetic growth curves ...
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Exponential Regression dependent variable with dummy variables or numerical average of each category?
My dataset includes toxin concentrations (continuous, dependent variable) for different size classes (5mm increments) of juvenile fish (categorical, independent variable). The smallest size class is ...
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Is Mean Square Prediction Error acceptable to use if predicted values are continuous but actual observed values are discrete?
I would like to compare the predictive power of 2 models. The models are meant to model count data, so the actual observed values are discrete. However both models are designed such that they output ...
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"Important" data points causing problems with nonlinear regression bootstrapping
I am trying to model radar backscatter of a planetary surface. The power which is scattered back to the instrument depends on the angle at which the it observes the surface. The shape of the resulting ...
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Multiple regression with two continuous predictor variables with R
I'm trying to find a suitable multiple model (with two continuous predictor variables) for my data and I'm not sure if a linear model with lm() would be sufficient ...
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PCA applied to non-linear data
Assume I apply standard principal component analysis to data, where the observed variables are non-linear functions of factors. That is I have a panel variable $Y_{i} \in \mathbb{R}^{N_{Y}} $, which ...
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Logistic regression with labels corrupted by known noise model
I am interested in knowing the "right way" to fit a binary logistic regression where the labels have been flipped with instance-specific noise probabilities that are known.
For the scenario ...
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How do I find confidence interval and what do I do with confirmation points?
I'm running a face-centered composite design with confirmation points. Am I correct in my assumption that the "S" I get from Minitab can be used to calculate CI? If not, how do I get the CI? ...
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GWR on spatially joined attributes
I am new here and am trying to build and compare a couple models using spatial data for estimation (GLM and GWR). I am trying to estimate counts using a training dataset that contains observations on ...
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Question on nonlinear least squares
Consider the following equation for $Y>0$:
$$
(1) \quad \log(Y)=\log(\gamma)+\log(\alpha+\beta X)+\epsilon.
$$
Assume that $E(\epsilon| X)=c\neq 0$. What are the consequences of this assumption on ...
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How to calculate attributions in a log-log model
Let's say I have a time-series oriented log-log model of the form ln(y) = B0 + B1ln(x1) + B2ln(x2). Let's say B0=1.5, B1=0.7, and B2=0.9. I use my model to make two predictions at different time ...
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Using MMD for Feature Selection with Linear Regression: Valid Approach?
I'm using Maximum Mean Discrepancy (MMD) for feature selection (i.e., to select the features that minimize the dissimilarity between the training and testing datasets). I'm aware that MMD introduces ...
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Interpretation and Analysis of a Multivariate Threshold Autoregressive Model
I'm looking to study the asymmetric affect a market rate, like the Fed Funds rate has on an interest rate. In other words, I would like to study the response of interest rate adjustments in different ...
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