Use this tag only for regression models (q.v.) in which the response is a nonlinear functions of the *parameters* (not because it's a nonlinear function of the *predictors*).

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12 views

Linear regression with prior on $\arctan \beta_1$

Suppose we have $\hat{y} = \beta_1 x + \beta_0$ (I ask only for the univariate case.) A typical Bayesian approach might involve Normal priors on both parameters. I was thinking today about a ...
0
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0answers
11 views

Generating Confidence & Prediction Limits in Extrapolation of Non-linear Regression

I am using PROC NLIN/NLMIXED to estimate parameters from some equation and then trying to use these estimates in a different equation to generate out-of-sample predictions. The problem is when I am ...
4
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0answers
23 views

How to calculate uncertainty in bacterial growth rates (or in the slope of any local regression)?

I'm using a plate reader to measure optical density of different bacterial strains so I can compare their responses (growth rates and changes in them over time) to stress conditions. The growth curves ...
2
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0answers
11 views

Does R's NLS always fail if model parameters are not identifiable?

This question from several years back describes the "singular gradient matrix at initial parameter estimates" error. The answers say the reason for the error is that the parameters in the model are ...
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0answers
15 views

Comparing nonlinear models using the Chi-squared asymptotic approximation

I am attempting to identify the best model fit for a nonlinear mixed effects model using the asymptotic approximation to the Chi-squared test. When calculating the appropriate number of degrees of ...
0
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0answers
12 views

Determining fit for potentially periodic non-timeseries data?

I am working with genetic data that has 100,000 wide regions with given diversity values attached to them, as below: ...
0
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0answers
9 views

Non-linear Variables in Excel [duplicate]

Regarding the following equation in excel regression: y = x + x^(-.5) + e I have made the equation nonlinear in the sense that I added to the data a new column which takes the values of x to the ...
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0answers
17 views

About the Randomized Dependence Coefficient

In the paper The Randomized Dependence Coefficient, authors introduce a novel dependence coefficient which seem to be quite generic and powerful compared to what is present in the literature. It is ...
0
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1answer
7 views

Unable to predict using bart() {BayesTree}

I used bart function from BayesTree library to build a model on my training data. It fits my training data very well. However, I'm unable to predict for the test set and check its performance. ...
0
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0answers
8 views

Picking the Right Co-Variance

I am conducting a repeated measures regression, in a linear mixed model. I am also analyzing the data with a general estimating equation. The dependent variable is continuous (a measure of arousal) ...
2
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1answer
37 views

Fitting nonlinear meta regression models to data

I have a collection of data, obtained from different studies. To plot the ratio of means against different CO2 concentrations, I used a random effects model with a continues predictor (the CO2 ...
7
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2answers
243 views

nonlinear regression two equivalent models on paper, but different estimated parameters

I measured one response variable Y1 as a function of two measured independent variables X1 and X2 It is common practice in ...
0
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0answers
23 views

Nonlinear first-stage specification in 2SLS

My question is whether there are any substantial benefits from specifying a nonlinear first-stage in 2SLS regression. Generally, we assume that first stage is a linear relationship, like: ...
0
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0answers
56 views

How to know which model is appropriate for my data?

I study regional planning. there is a theory that says population density (D) is changing by distance to CBD (center of city). And the model for any city is different. And I have population density ...
0
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0answers
15 views

Transforming independent variables in R [duplicate]

Is there a way through which we can determine how can we transform our independent variables to increase linearity. I am aware of boxcoz function but it provides information on transforming dependent ...
0
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0answers
19 views

AIC for multiple nonlinear regression models

How do we got about using AIC for multiple nonlinear regression models ? For example: If i have a dataset with N instances, and they can be explained by a collection of 3 models where each model has ...
1
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0answers
61 views

Gini Coefficient - Variable Importance Measure

There is a whitepaper for selecting important variables in a linear regression model. The URL of the whitepaper is http://support.sas.com/resources/papers/proceedings15/3242-2015.pdf . It explains ...
0
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0answers
19 views

reason codes for non-linear models?

I have a non-linear model with n variables (ANN model). The variables are WOE-transformed to train the model. I have a test record scored using the non-linear model mentioned above and it is in the ...
0
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1answer
17 views

Testing the utility of adding a continuous variable to a nonlinear regression.

Let’s say I have the hypothesis that soil fertility affects the relationship between weed biomass and crop biomass. One way to go about testing that hypothesis might be to model the relationship ...
4
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4answers
208 views

How can I test a nonlinear vs a linear regression model?

I've got a panel regression model where the Ys assume a curved shape when plotted over time. A histogram of the residuals shows they are normally distributed but a residual-vs-fitted plot shows a ...
1
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2answers
52 views

multivariate mean?

[edit : because my question was ambiguous, I decided to rewrite it entirely, with some simplification but a lot more details on the experimental design] Four independent 10m*10m plots each received ...
0
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1answer
32 views

Assessing variable importance from coefficients and p-Values of linear regression

Is it correct to say that if the significance of a variable is very high (p<0.001) and the coefficient is very large that the variable is important in a general sense? If not can you give an ...
3
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1answer
43 views

OLS estimation for Nonlinear model

Consider the following model which may be nonlinear: $Y_{t} = f (X_{t}, \beta_{0}) + \mu_{t}, \hspace{0.2cm} t=1, ..., T$ If we assume that: $\mu_{t}$ i.i.d with mean = $0$ and ...
1
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1answer
48 views

What polynomial do I need for regression of such relations

I have following 4 graphs and for each I have to do regression. The relation is clearly curvilinear. What term should I use for regression (eg y ~ x+x^2) for each of these?
6
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1answer
245 views

Examples of Non-Linear Time Series?

Does anyone have an example of real world (ideally multivariate) time-series data that depends on its past in a non-linear, but additive way? I understand that there are several examples of ...
3
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2answers
60 views

Understanding Big/Little $O_p$/$o_p$ Notation for Estimators

I am reading a Text about Single Index Models (SIM), where a SIM is defined as $E[Y|X=x] = G(X' \beta)$, with $G$ and $\beta$ unknown. After proposing an estimator for the function $G$, the ...
0
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1answer
47 views

Visualizing nonlinear regression

I have following model using mtcars dataset: ...
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0answers
30 views

Methods for detecting nonlinear relationships among variables

I believe that some of my independent variables vary nonlinearly with my dependent variable. I know of a couple of ways to determine the possible type of function, but I'm wondering other methods, and ...
2
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0answers
68 views

Difficulty with logistic regression: logit transformation is non-linear

I am trying to perform a logistic regression to model likelihood of receiving a procedure given a certain diagnosis. There are several covariates to analyze but one of the main ones is patient age. As ...
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0answers
28 views

Interpreting GNM/GLM regression using R

I have a training dataset that contains input features. It's an input file which is tsv separated. I had input data for 200 rows with 4 cols. That last col is the target variable. I have also loaded ...
0
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1answer
57 views

Difference between SAS and R results - Nonlinear Regression

Hoping someone can assist with this rather niche question. I have a routine built in R for analysing depreciation curves, which is fit according to a logistic model with an additional additive ...
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0answers
29 views

Predicting time to failure with time varying cofators

The Goal I am modeling Hospital Length of Stay. More specifically, I would like to predict the number of days until a patient is discharged given all of the patients clinical factors throughout ...
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2answers
79 views

Regression analysis low R2 value - Result interpretation

When I run linear regression on my test data I get the following report: You can find the test data in here. The graph of actual vs predicted looks like: I would like to know if this is fairly a ...
4
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3answers
51 views

Hypothesis Tests for Non-linear least squares coefficients

I have a set of results that take the following form: ...
0
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0answers
14 views

Indicator regressions: standard errors

Hi I am estimating a regression of Y on X where both Y and X are indicator variables, that is, they take values 1 or 0. The model is $Y=\beta*X+\epsilon$ (without constant). I use OLS to estimate the ...
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0answers
13 views

Contributions of variables in Log-Log models [duplicate]

I have built a model on log(units) sold and want to measure the contribution of each independent variable in the model (which are also in ...
1
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1answer
48 views

How to fit intensity peaks from a image?

I have an image that I can convert to a text file / table of intensities. In this image, several regions show higher intensity, i.e., a peak. A peak may be stored in a 10x10 matrix with rows and ...
0
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1answer
55 views

Extremly poor polynomial fitting with SVR in sklearn

I try to fit an obvious around degree 5 polynomial function. Much to my despair, sklearn bluntly refuses to match the polynomial, and instead output a 0-degree like ...
3
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1answer
50 views

Predictive distributions in Poisson regression

I have the following question about Poisson regression. In "regular" (OLS) linear regression it is fairly easy to prove that the correct predictive distribution will be a $t$ distribution, but what if ...
0
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0answers
21 views

R-Squared in a non-linear model [duplicate]

I am running a dynamic demand model (a non-linear model) in SAS. My model includes three equations which should be solved simultaneously. I am applying a Iterated Seemingly Unrelated Equation (ITSUR) ...
1
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1answer
38 views

Multiple regression approach strategies for non-normal dependent variable

I'm hoping to analyze the influence of a set of variables on a continous dependent variable (between 0-1). The independent variables are a mix of both categorical, continuous and discrete features. ...
0
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0answers
36 views

Trying to fit single layer neural net with R's nls (nonlinear least squares) function

Working on building a neural network modeling frame using graph objects in R. I have a data set on passengers of the Titanic, modeling binary "survived" variable against continuous "fare" and "age" ...
0
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2answers
41 views

How can I make this biological relation into a glm model?

I have a biological relation: Y/m = (X * b) / (1 + X * b) where Y and X are variables, m and b are parameters. m is greater than Y, and everything is greater than 0. I have some training data with ...
0
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0answers
11 views

Finding redundant parts of a data set for training a nonlinear model

I like to train a nonlinear model based on a data set $D_1$,..$D_n$, where $D_i$ is collected by doing experiments $E_i$. It is possible that $D_i$ is redundant given $D_j$ and $D_k$. Since doing an ...
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0answers
24 views

Interpreting p-values of log regressions

The following output is for a log log model. ...
0
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0answers
21 views

interpret normal probability plot of residuals [duplicate]

I am looking at two normal probability plots of some residuals from a two different regressions. I am trying to make sure I fully understand what they are telling me. The first chart below appears ...
0
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0answers
85 views

What is the difference between Average Partial Effects (APE) and Average Marginal Effects (AME)?

In this answer, the terms Average Partial Effects (APE) and Average Marginal Effects (AME) are used interchangeably. But in this paper, the terms are used to mean different things (page 75). But it's ...
7
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2answers
516 views

How to tell the difference between linear and non-linear regression models?

I was reading the following link on non linear regression SAS Non Linear. My understanding from reading the first section "Nonlinear Regression vs. Linear Regression" was that the equation below is ...
1
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1answer
70 views

How should one assess model fit for non-linear regression?

I am looking at non linear regression. Below is some example output from a non linear regression using MATLAB. There are also two links below this output from the Minitab website. The links explain ...
4
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0answers
49 views

F-test to determine whether more than two sets of data differ

Here is the context for my question: I understand that you can fit the same model to two different datasets separately and then fit the model to the datasets pooled together as a way to discern ...