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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.

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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. ...
Chao's user avatar
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1 vote
1 answer
41 views

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 ...
fsp-b's user avatar
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12 views

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 ...
wdkrnls's user avatar
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2 votes
1 answer
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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 ...
96jtaylor's user avatar
2 votes
1 answer
21 views

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 ...
Astral's user avatar
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0 answers
30 views

"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 ...
ampny's user avatar
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0 votes
1 answer
52 views

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 ...
Mogens's user avatar
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1 vote
0 answers
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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 ...
fes's user avatar
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6 votes
2 answers
259 views

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 ...
ted's user avatar
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0 answers
23 views

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? ...
Ladislav Révay's user avatar
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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 ...
Juan P FZ's user avatar
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0 answers
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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 ...
Star's user avatar
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0 answers
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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 ...
Gaurav Bansal's user avatar
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0 answers
16 views

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 ...
Adham Enaya's user avatar
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0 answers
9 views

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 ...
dsupin's user avatar
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0 answers
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Non-linear effects (rc splines) in competing risk regression

I use the {tidycmprsk} package to fit competing risk regression models (death/transplantation as competing risk). E.g.: ...
sjg's user avatar
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1 answer
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How to properly assess relationship between response and time as a predictor when there is information on both seasons and month of the year?

I would like to be able to test either via a linear mixed effects model or a hierachical GAM (where appropriate) to see whether there is a relationship (either linear or non-linear) between ...
aim6789's user avatar
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7 votes
3 answers
615 views

How to analyze a dichotomous outcome with 50% missing data?

I am researching predictors of dropout from a training program. I want so to see if personality traits add incremental variance above well-established predictors like age, fitness, and education. So, ...
E_H's user avatar
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1 vote
0 answers
18 views

Competing Risk or Logit Model?

I have time to event data with competing events and plan to use a competing risk model for my analysis. But what are the benefits in compairson to using a logit model and adding a dummy variate that ...
KC15's user avatar
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1 answer
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Performance of GAM with asymptotic regression curves

Our research group studies edge effects of disturbances on adjacent relatively undisturbed ecosystems. We evaluate metrics such as light levels that may be higher at forest edges relative to the ...
user3386170's user avatar
0 votes
1 answer
33 views

Is it possible to model a continuous variable using a linear spline with one knot located at two different place according to a categorical variable?

I’m trying to understand what there is behind an equations currently used to estimate glomerular filtration rate (GFR). This equation was derived using linear regression where ln(GFR) was modelled ...
Paolo Tolomeo's user avatar
2 votes
1 answer
55 views

Build a logistic model that fits two curves in R

The relation between my dependent variable y and my independent variable x can be described by a logistic regression like: ...
unknown's user avatar
  • 95
0 votes
0 answers
15 views

non-linear correlation or finding thresholds for changes with relatively few data points

I'm a novice at stats and thought I'd ask this question to those with much more experience than me. I've got temperature data and count data for number of animals hibernating in man-made boxes. The ...
Bethy's user avatar
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0 answers
18 views

Target variable directly dependent on feature

Suppose a target of interest A and a feature B which serves as a good predictor for A. (both continuous variables) In literature, the target is always reported as C := A/B however. For me it is not ...
dinaue's user avatar
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1 vote
0 answers
30 views

Prediction interval as per probability

I am practicing the regression problem using the sci-kit learn dataset. The dataset is about housing prices. When we use a regression model, it predicts a number. Based on the predicted value and ...
Bad Coder's user avatar
1 vote
1 answer
165 views

Bimodal Posteriors in Bayesian Non-linear Regression

I will preamble by saying thank for reading this, and that any commenter should feel free to criticise any aspect of this modelling specification or workflow you deem naive or uninformed. I am ...
MDZ's user avatar
  • 31
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0 answers
30 views

Global sensitivity indices with sign (positive/negative)

I’ve used Sobol global sensitivity indices to analyze the effect that some input variables have on a nonlinear regression model, but now I need to know if the effect is positive or negative (i.e. if ...
Ken Grimes's user avatar
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0 answers
23 views

Does multivariable quantile regression model with mixed effects require fractional polynomials for independent variables?

Hello Cross Validated community, I am currently working on a project involving a multivariable quantile regression model with mixed effects, where the objective is to explore the relationships between ...
Mikołaj's user avatar
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0 answers
15 views

Nonparametric Regression with Convolution Responses

Here's the problem I have. I have a set of measurements $f_i \pm \sigma_i$ as a result of some measurement process. In reality, there is a continuous signal $g(x)$ for $x \in D \subset \mathbb{R}$ ...
Melkor's user avatar
  • 1
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0 answers
35 views

Derive LM test for linearity

I am studying nonlinear models. And I try to derive LM test for nonlinear model against well-specified parametric linear models The book gives score vector $s(\theta)$ and information matrix $I(\...
1190's user avatar
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30 views

Is this the correct hypothesis of softmax regression?

Consider a dataset of $m$ training examples, $n$ features and $K$ classes. So we have a feature matrix $\mathbf{X} \in \mathbb{M}_{m, n}(\mathbb{R})$ and a weight matrix $\boldsymbol{\Theta} \in \...
Sagnik Taraphdar's user avatar
7 votes
1 answer
152 views

Minimal number of features and observations for random forest regression analysis?

Linear regression is a suitable regression method even for small numbers of observations as long as there are enough observations per predictor (with factors 5 to 15 given as rules of thumb) and we ...
Bernhard's user avatar
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1 vote
0 answers
56 views

How to fit non-linear mixed model for count data?

I have been reading this paper and its supplemental coding materials on how to fit a non-linear mixed model for a use case that is very close to a use case I am interested in modeling: ...
Alex Braksator's user avatar
3 votes
2 answers
124 views

Remove effect of an independent variable on a dependent one

Currently, we are taking weight measures from a brick with a weight of 1kg as a way of calibration. However, the final weight returned by the sensor is not 1kg, instead, it varies along with ambient ...
anttphy's user avatar
  • 31
6 votes
2 answers
229 views

Is there a technique similar to multiple regression which does not require linearity?

Background: We have biological gene expression data of cells (from a single-cell experiment). So, the data is in the form of a gene by cell matrix, where each value is the expression of a specific ...
Sam's user avatar
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1 vote
0 answers
25 views

Applying a non-linear model to assess the difference between variables [duplicate]

I have already asked a similar question on this site (Correlating two variables / convergence); however, I feel like the way I wrote the previous one made it a bit too ambiguous, and on top of that, ...
curious's user avatar
  • 31
2 votes
0 answers
108 views

Applying a nonlinear regression model to assess the difference between variables

I have two variables which represent attitudes towards slightly different sorts of people, though measured with the same scale. I also have a superordinate variable which is a certain psychological ...
curious's user avatar
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0 votes
0 answers
68 views

Error in nls model: singular gradient matrix at initial parameter estimates. Cannot figure out appropriate start values [closed]

I'm trying to implement equation 3 from this article research paper, for ease here's a screenshot of the equation: Here's my code for it ...
Favour Onyido's user avatar
1 vote
1 answer
61 views

Sklearn Gaussian Process Regressor Overfitting

I am testing a set of regression algorithms and I'm having troubles with GPR. I have a set of 60 observations x 101 variables as a predictor (X) versus a set of 60 observations x 1 variable as a ...
Mutewinter's user avatar
0 votes
0 answers
43 views

Polynomial regression with multiple independent variables

I have data set with 3 indepdent variables and 1 dependent variable. They are related to each other in the following way. y* = a q^{1/3} + b q R C_{NS} + c where a,...
Khushal's user avatar
  • 111
1 vote
2 answers
79 views

Finding the temperature value that gives optimal value

I'm trying to analyze some sleep data from kaggle (this example data does not have correct temperature data but the actual data I will use in the future will have precise temperature) to try to find ...
pato's user avatar
  • 11
0 votes
0 answers
17 views

Knots in regression and the dummy variable trap

I am running a knot-like type of regression and have a couple of questions: Imagine that we are working with daily data that spans over $3$ years. Consider the following model: $y_t = \beta_{0, t} + ...
richard baws's user avatar
2 votes
0 answers
17 views

Standardizing OR from logistic regressions with log-transformed variables for meta-analysis?

I´m trying to meta-analyze odds ratios from logistic regressions; some of which log-transformed the independent variable first. (i.e. some studies present an OR per +1 in the independent variable, ...
san festein's user avatar
3 votes
0 answers
124 views

GAMs falsely suggest non-linear function (edf>2) for about 15-25% of simulated data?

I've been learning about GAMs and was curious about how sensitive they are to noise within a given sample. I know people generally say that GAMs are more prone to overfitting than GLMs and need to be ...
Victor Pokorny's user avatar
3 votes
3 answers
88 views

How to visualize trends

I am working on a paper where we plotted BMI trends as a function of age in the population. We plotted trends for six databases, then we plotted for each sex, then for race, in three categories. I ...
Stefano Staurini's user avatar
1 vote
1 answer
21 views

How to diagonalise when there is less parameters to estimate than data in the Levenberg-Marquardt algorithm

I am trying to calibrate a Heston Model with 100 call options using this paper https://arxiv.org/pdf/1511.08718.pdf. In algorithm 4.1 on page 18, they define the dampening factor as: $$\mu_0 = \omega \...
THATS MY QUANT MY QUANTITATIVE's user avatar
1 vote
0 answers
69 views

How to calculate the uncertainty of fitting parameter in a nonlinear model

I have a cost function which is: (F(X,B)-Y)*(F(X,B)-Y) F is my model, B are my fitting ...
MOON's user avatar
  • 173
2 votes
1 answer
65 views

Which of these regression models would be the most appropriate to test the given research question?

If we want to test: population with a high level of education were less likely to contract COVID, which of the following regression(s) we should run? Explain whether each one is appropriate. LN is ...
Dannis's user avatar
  • 23
0 votes
1 answer
37 views

Predicting CSAT scores using GAM

I am trying to fit a GAM model to predict CSAT (customer satisfaction scores on a scale of 0 to 10) which I believe are ordinal in nature. I used a categorical predictor (type) and two numeric ...
Math Lover's user avatar
3 votes
1 answer
138 views

Compare non-linear model parameter estimates between two groups with random effects

I have a treatment (2 groups) for which I want to compare parameter estimate of a sigmoid curve (i.e., do any of the three parameters differ significantly between my two treatment group). I am ...
miki's user avatar
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