Stack Exchange Network

Stack Exchange network consists of 175 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Visit Stack Exchange

Questions tagged [fitting]

The process of fiting some statistical model to a particular set of data. Mostly done on a computer, and using varied numerical methods such as optimization or numerical integration, or simulation.

2
votes
0answers
26 views

Working out a metric for the goodness of fit for 2D data in time

I have a dataset which I wish to optimise a fit for. The data might look something like I.e. orange is t=0, blue is t=1, and green is t=2. I wish to find a fit. I have a differential equation ...
0
votes
0answers
12 views

Model that fits ellipse for pupil detection

I have an eye tracking system which fails to correctly detect the pupil in the camera image when there is a larger luminance gradient across it. This happens if the pupil is very dilated. For me it's ...
3
votes
2answers
91 views

Male-female height difference estimation from thresholds

Introduction. Assume that two populations $A$ and $B$ are distributed with normal distributions $N(\mu_A, \sigma_A^2)$ and $N(\mu_B, \sigma_B^2)$. This is a general problem, but as an example, I will ...
0
votes
1answer
18 views

Fitting a multivariate Gaussian with extremely sparse samples

We have a multi-variate Gaussian distribution. For instance with 3 variables. The correlations between the variables are important! We are fitting it to data, however, the samples are such that each ...
1
vote
0answers
20 views

Should the reduced chi-sq of a fit to some points be the same as the reduced chi-sq of a fit through a weighted average of those points?

I have nine data points with three of them taken at x = 2, three taken at x = 4, and three more taken at x = 6. I came up with a straight-line fit for these points, and found the reduced chi-sq of ...
0
votes
0answers
4 views

Finding expected range of values in multi variable reduction

For a least-squares reduction to find expected values for $a,b,c,d, ...$ from a number of equations like: $a + b = n_1$ $a + c = n_2$ $b + d = n_3$ $c + d = n_4$ ... My question relates to ...
5
votes
1answer
96 views

How to improve this time series model?

I am trying to fit a time series model for household data which is a time series variable. Initially my data looks like this, Since the data does not seem to be stationary I differenced the data. ...
1
vote
0answers
14 views

Distribution identification with respect to number of fitting parameters

I want to check a sample of measurements against different distributions in order to determine the underlying distribution. Distributions should be yielded based on probability or goodness-of-fit ...
1
vote
0answers
52 views

Is it possible to fit a binomial cumulative distribution with only three data points in R?

I have three data points (0,0), (30,0.1538), (60,0.4914). Is it possible to fit a binomial cumulative distribution function only using these three points in R? If not, what sort of extra information ...
0
votes
1answer
34 views

Determining error between two surfaces given same discrete inputs?

Apologies if this isnt the best SE forum to ask on, but it seems relevant here. I have, as an output of a machine learning algorithm, a surface in z, which has known increments along x and y. These ...
1
vote
0answers
22 views

Uncertainty from equation involving fitted parameters [closed]

I want to estimate the uncertainty of a calculation which involves a quantity from a fitted mathematical model. More specifically, the end calculation would be something like: P = x / A_tot where I ...
1
vote
1answer
45 views

Fitting the Poisson distribution to binary data

I'm studying several time series. The variables of interests are dummy variables which take value 1 when a certain event happened, 0 otherwise. I want to find the best distribution to describe these ...
0
votes
0answers
19 views

Inestability of BIC when selecting nested models

Currently I am working with spline regression and a method for selecting knots adaptively. My method gives me a set of potential knots that generally has a large number of elements. Following He et al....
2
votes
0answers
27 views

R - Fitting a Coxian phase-type distribution to data

Using R, I would like to fit a Coxian phase-type distribution to a vector of waiting times ($t_i$) where the $\mu$'s and $\lambda$'s are unknown i.e. I want to find ...
0
votes
0answers
9 views

Fitting of 2D data [duplicate]

I have a 2D data-set. If we plot, it looks like above. Now if I want to know the central value of the distribution $\theta$ and width of the distribution $W$. How do I do that?
1
vote
0answers
8 views

When making a fit to data without weights, how reliable are the fit errors?

I often need to fit data which is a spectrum. And it isn't possible to have many identical spectrum from which to produce error bars on the individual points in an averaged spectrum. So my question ...
4
votes
2answers
68 views

Change variable to log transformed or keep original?

A log transformation of the dependent variable is sometimes recommended as a remedy for some cases of non-normal distribution of residuals after fitting a linear regression model. What is the proper ...
2
votes
0answers
33 views

Do mismatches in areas of peak density affect the KS-test more than mismatches in low-density areas?

In the following plot you see my empirical data (black) plotted against a hypothesised distribution (blue). However, a KS-test shows that there is no indication that my sample follows this ...
2
votes
0answers
34 views
1
vote
0answers
31 views

Plot confidence interval of average polynomial fit from coefficients

I am studying the propagation of a wave and to do that I want to follow the position of my maxima over time. In order to do that I thought to fit a polynomial on the data of each replicate, then save ...
3
votes
0answers
63 views

Are $h_i(x)=x^{-\alpha_i}$ okay basis functions for fitting?

I have some pairs of data ${(x_1,y_1),..., (x_n,y_n)}$ genereated by some process and would like to fit it with a function so that $y_i \approx \hat{f}(x_i)$. By plotting the $(X,Y)$ on a 2D plot, ...
1
vote
1answer
52 views

Based on the ideas of Parameter Estimation and Fitting Probability Distributions, what stops us from making any function be a PDF(PMF)?

Currently I am doing an introduction to parameter estimation and fitting probability distributions to sets of data. So in a small synopsis what I understand the whole process to be like is the ...
4
votes
1answer
419 views

Why we do not accept the result of our simulation study as evidence of a limitation of one method

I am doing a mixture model. I have established a new method using EM-algorithm. I have simulated data from a mixture model. Then, I applied my new method to the data. The result is very satisfying. ...
1
vote
3answers
121 views

In Bishop's textbook, is the example of overfitting exaggerated?

Here, the data $x$ are randomly generated, and $t$ are generated by running $x$ through a function $\sin(2\pi x)$, then Gaussian noise is added. Bishop's text then tries to fit those data using a ...
0
votes
3answers
57 views

How can I understand the concept of a noise in machine learning?

In Bishop's book, one of the first examples is shown here Essentially, the data $x$ are randomly generated, and $t$ are generated by running $x$ through a function $\sin(2\pi x)$, then Gaussian ...
1
vote
1answer
21 views

How to fit a superimposed distribution (\eg a Gaussian distribution + a Uniform distribution)

Suppose we have a set of independent observations of a random variable X, which is a Superimposition of two mutual independent random variables (i.e. X = Y + Z), Y follows a uniform distribution, ...
0
votes
1answer
67 views

R - how to estimate shape and scale parameters of Weibull distribution for claims development factors

I have a set of insurance data. Development factors fall with period, so follow Weibull distribution. I want to estimate Weibull parameters and smooth Development Factors. If I estimate parameters of ...
0
votes
0answers
18 views

Fitting wrong copula type to a real data set

I have developed a new mixture copula model. This model overcomes some limitation of copula models. I tested my new model on a simulation data. The model shows a superior result. My supervisor asked ...
0
votes
1answer
35 views

Inferring GMM parameters with Gibbs Sampling

On my book, "Machine Learning A Probabilistic Approach". It's stated that is straightforward to derive a Gibbs sampling algorithm to fit a mixture model, especially if we use conjugate priors. So ...
2
votes
0answers
51 views

How do I fit a cumulative Gaussian distribution in R? [closed]

I am trying to fit a cumulative Gaussian distribution function to my data, but I'm not sure how to do this. From what I understand, the fitting process tries to find the mean and standard deviation of ...
0
votes
0answers
22 views

Model fit for StMoMo

As a statistics newbie, I am trying to model mortality. I grabbed majority of the code from the package Vignette, and fitted the data. However, model fit does not seem to be great in my reproducible ...
0
votes
0answers
50 views

Compute log-likelihood from sum of squares?

I have fit a 2D Gaussian to a surface in Matlab and need to compute the log-likelihood of this fit. Can One use the sum of squares between the Gaussian model and the actual surface to compute the log-...
0
votes
0answers
17 views

AR(1)-GARCH(1,1). A bad fit with log likelihood?

Consider these two DCC models: ...
6
votes
3answers
145 views

Fit data to parametric distribution

I have data with nice bell-shaped histogram PDF. However, the Normal distribution fitting (by calculating mean and variance) does not work as the figure below. My question is that if there are other ...
0
votes
0answers
35 views

Best way to model the dependency of these two random variables (copula?)

I'm modelling the joint PDF of two variables that looks like this , where vt and vr are the random variables. The dashed line shows the joint pdf assuming they are independent (the product of its ...
1
vote
1answer
32 views

Choosing model for feature selection on categorial data

I have a dataset composed 30 features and 1 response. My response is 0 or 1 and, all of my features composed three status includes = -1,0,1. I wanted to do features selections in R, firstly I want to ...
-1
votes
2answers
86 views

How can I do a fit for negative $y$-data, which has exponential phenomena? [closed]

How can I do a fit for negative $y$-data, which has exponential phenomena? Such as: ...
1
vote
0answers
26 views

Proof that sequential GARCH fitting is not efficient?

I've read that authors like Tsay (as well as several other researchers) use a sequential method for fitting a ARCH-type model. This means first estimating the conditional mean model (ARMA-type) and ...
2
votes
0answers
45 views

Data Transformation to achieve Linearity

One assumption of OLS regression is Linearity. To check whether the assumption holds, you can plot component + residual plots or partial residual plots. When a linear relationship is apparent, is's ...
1
vote
0answers
44 views

Fitting a multiple linear regression in R [closed]

I have annual mean temperature and precipitation data from 1901 to 2015: I want to do a multiple linear regression: ...
0
votes
0answers
35 views

Estimating posterior probability from a random grid

I am simulating the evolution of galaxies, and want to find the distributions of input parameters that best reproduce an observation of a particular galaxy. I have a measurement $y \pm \sigma$ of a ...
1
vote
0answers
27 views

r - Poor model fit with StMoMo package on Human Mortality Database

I am fitting USA mortality data from Human Mortality Database (with data downloaded from here after registering for a free account https://www.mortality.org/cgi-bin/hmd/hmd_download.php) with the ...
0
votes
0answers
18 views

Measure goodness of fit between a model that has error and data with error

Is there a way to express how well a model matches data where both have uncertainties? I looked for other examples that may capture this, but was unsuccessful in finding one. If you happen to know of ...
1
vote
0answers
38 views

How to detect an increase in a loess model fitted value at the end

Sorry if the question is trivial, but I'm not finding a proper idea for this issue. I'd like to find if a series of fitted value of a loess is increasing in the end. I'm working with some data like ...
0
votes
0answers
27 views

Fitting power law with loglog or exponential? [duplicate]

I have $x$- and $y$-data, and I want a power-law fit ($y=ax^b$). I always fit $\log(x)$ and $\log(y)$ by $p_1x+p_2$ (Matlab poly1), but when I fit $x$ and $y$ with $...
0
votes
1answer
31 views

$2D$ Maximum Likelihood Fit

I have read a couple of places that it is possible to do a $2D$ (or $3D$) maximum likelihood fit, but I can't seem to understand how this would work. Suppose I'm considering a probability distribution ...
0
votes
0answers
51 views

Compairing the fit of quasi-Poisson and negative binomial models

Is there any way to compare the fit of quasi-Poission and negative binomial models in R?
1
vote
0answers
36 views

How to understand the results of nonlinear mixed-effects regression model

I have some data, obtained from 4 different groups. Each repeat is some 4 parametric sigmoid. I need to fit the data to sigmoidal function and answer the question, whether sigmoids are different ...
0
votes
0answers
40 views

How to solve systems of linear equations with random variables? How to identify model parameters?

I want to learn know how to solve systems of linear equations with randomness. Example of a deterministic version of the sort of problem I want to solve: ...
2
votes
0answers
217 views

Bayesian networks with continuous variables in Python [closed]

I am trying to create a Bayesian network model (Probabilistic graphical model) in Python, that can handle continuous data. I have tried using pgmpy, but the 'fit' function in pgmpy has not yet been ...