A statistical model is a formalization of relationships between variables in the form of mathematical equations. A statistical model describes how one or more random variables are related to one or more random variables. The model is statistical as the variables are not deterministically but ...

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6
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1answer
25 views

When to use offset() in negative binomial/poisson GLMs in R

I'm trying to detect relationships between species abundances (counts) and time (years) for many species using either Negative Binomial or Poisson regressions (depending on degree of dispersion). ...
-4
votes
0answers
68 views

model development or equation making [on hold]

I have three parameters $a,\ b,\ c$ and resultant $y$ Independently y depends on $a$ as follows. $y = a ^3 + 3 a^2 + 4 a + K $ $a$,$b$ and $c$ varies in coded form here $a$ varies from $-1$ to $+1$ ...
2
votes
1answer
13 views

What's the best way to model two insurance categories that are non-exclusive?

I am modeling insurance status in a logistic regression as separate dummy variables for private, Medicare, Medicaid, uninsured, etc. For people that are dual eligible, should I have a separate "dual ...
0
votes
1answer
44 views

Partitioning explained variance to fixed effects by comparing r squared (R2) between linear mixed models

Lets say I have 2 linear mixed models. One is simply a subset of the other. The first contains terms for 2 fixed effects and a random intercept. One of the fixed effects, "x1" I know, a priori, ...
3
votes
1answer
42 views

Modeling a process with decay and refilling

My question is about the approach that needs to be taken for modeling a particular process with . I have looked around for similar questions or answers but didn't find any. I got some links to Markov ...
3
votes
0answers
20 views

Assumption about Systematic Errors

In Maronna, Martin and Yohai's Robust Statistics (2006, p.17), they describe a location model as follows. $$x_i = \mu + u_i,$$ where $x_i$ is the $i$th observation; $\mu$ is the hypothetical mean ...
0
votes
1answer
11 views

Comparing effects of different methods when each method has multiple levels

I'm working in retail and we are trying to determine the effect (on units sold) of reducing an expense for a set of products within a product group. We have two methods of reaching this goal. One is ...
0
votes
0answers
18 views

Decision Tree Modelling

Can anyone explain me about Decision tree parameters - minSplit, minBucket, Complexity, minDepth with some simple decision tree example? And how this parameters will affect the accuracy measure? ...
2
votes
2answers
69 views

Modelling Technique

I have a 5 years of Cargo insurance (goods transportation insurance) data. I need to predict the claim amount based on their policy date and some other variables like mode of transportation, Country ...
3
votes
1answer
133 views

Zero-inflated negative binomial models: why not use two separate models?

Zero-inflated negative binomial models have two components: a count component (negative binomial regression part) and a zero component (logistic regression part). Why not just run two separate ...
3
votes
3answers
38 views

Managing complex models/formulae

I'm building a logistic model on a fairly large dataset (~90 features). I have enough data to include many different features, nonlinearities and interactions between them without worrying about ...
1
vote
1answer
34 views

Analysing the residuals themselves

As far as I know, it is possible to fit a linear regression model and then fit a second model to predict the residuals from the first model by using some other variables. By this you can understand ...
1
vote
1answer
160 views

what if response variable is 'yes or no' in R?

How to analyze above the data to predict the probability that people have disease with a model? Factors thought to influence infection include city, age, and diet. BUT, I don't know how to do ...
-1
votes
1answer
42 views

How many observations do I need to implement ARIMA?

I need to model an ARIMA with a time-series data. But my data is the statistics of land area, and it's annual data, so I have 64 points between 1950~2014. Because it increased by a stable rate, So I ...
0
votes
1answer
21 views

Principled way of combining time series with different spans and granularity into an econometric model

I want to forecast the price of something given various time series as inputs. The problem is that they are of different frequency (annual, quarterly, monthly, daily) and time periods (the more ...
2
votes
1answer
26 views

How can I use the set of linear models to obtain a single equation?

This is my new attempt to rewrite the previous question about combining a few linear regression models into single equation. The background is that I have a set of dependent variables Y which is ...
0
votes
1answer
35 views

Are level 1 and level 2 residuals in a mixed effects model always normally distributed?

Take this mixed effects model: $y_{ij} = \beta_0 + \beta_1X_{ij} + \mu_{j} + \epsilon_{ij}$ The level 2 residuals are $\mu_{j}$ and the level 1 residuals are $\epsilon_{ij}$. As I understand the ...
1
vote
0answers
20 views

Specifying a structural equation model with sem

I'm new to sem package and sem analyses, so this is probably very basic, although I was not able to solve it myself reading some other similar posts. I was trying to specify a structural equation ...
0
votes
0answers
12 views

Two ways to model pre/post/treatment setting. Which one is preferred and why?

I have 20 individuals randomly distributed into two groups(treatment vs non-treatment) and test_score was measured before/after the treatment. My central goal is to measure the effect of the ...
1
vote
0answers
14 views

Find distribution for a dataset $\mathbf{x} = \{x_1, x_2, … x_N\} \in \mathbb{R}$

Assume I have a dataset of $N$ observations $ \mathbf{x} =\{x_1, x_2, ... x_N\}$ where $\{x_i \in \mathbb{R} | 0 \leq x_i \leq 1\}$ and I want to find out how they are distributed. Is it possible to ...
3
votes
0answers
26 views

Does a well-fitting model have shallower chi-square minima than a poorly fitting one?

I am trying to fit some data with a range of models with some variable parameters, to determine which of the possible models best describes the data. I have noticed that if the model is a poor fit, ...
1
vote
0answers
13 views

Approximating a random field

I have got a bunch of $n$ ($\approx 100$) pixelized maps. Each pixel is a single figure. Each so-called map can be represented by a matrix whom each element is a pixel. Let's say that there $p\times ...
7
votes
2answers
128 views

A person repeatedly selects the two most similar items out of three. How to model/estimate a perceptual distance between the items?

A person is given three items, say pictures of faces, and is asked to pick out which two of the three faces are the most similar. This is repeated a large number of times with different combinations ...
0
votes
0answers
39 views

Methodological advice

I need some help validating the statistical methodology I'm trying to use to analyze some data. My data is from a repeated measures study where each participant did two activities. Half of the ...
1
vote
0answers
13 views

How to separate model errors from measurement errors?

I've got data measured with errors that have known measured_RMS. I am testing a model that has model_RMS differences between model prediction and measured data. What would be a reasonable estimate of ...
0
votes
0answers
16 views

Methods for predictive modeling on continous target

I am trying to put a continuous target into predictive modelling method. The target is an amount that can range from 0 to unknown. I have roughly 1000 records (for modelling and validation ...
0
votes
0answers
20 views

Graphing GEE Models in R?

I am currently working on a biological dataset where I am attempting to fit a detection function (Detection probability as a function of distance). I have been using the package geepack (in R-studio) ...
0
votes
0answers
11 views

Need Insight on Measuring Variable Effect

I apoligize in advance for the wall of text. I'm working on a project in which I have been asked to determine what factors influence employees leaving our company. Also, it would be useful to be able ...
0
votes
0answers
11 views

Modeling Switch-Like vs Continuous Signal

I'm currently trying to figure out the best way to model a given scenario, but am unsure of the best way to go about it, so I'll explain the situation in as much detail as possible. So I work in a ...
0
votes
0answers
10 views

Need advice on how to model this scenario in NLME

I am wondering how to analyze this scenario using non-linear mixed effects. I am a working at the novice level, and will have access to the Pinheiro and Bates book through my university library soon. ...
0
votes
0answers
6 views

How to adjust logistic models in case bivariate analyses encounter issues?

The model looks like: Y = X1 + X2 + ... + Xi Y is categorical or, to be exact, ordinal Xs can be categorical (dichotomous, ordinal, nominal) or continuous I first run bivariate analyses between Y ...
3
votes
1answer
77 views

Studentized residuals undefined

I am wondering if anyone could explain why there are some states where Studentized residuals are undefined. For example I got the following R code: ...
1
vote
1answer
11 views

Problem fitting a geeglm regression

I am fitting a model using geeglm in geepack and ran into a problem. I have a dataset pertaining to oil consumption and fit the below model. ...
0
votes
0answers
27 views

Time series: What is my natural time period?

We are modeling univariate ts with R. Sampled daily since 1-1-2013 at five observations per week. We are unclear about how to decide 'natural time period'. Until now we just assumed 260 weekdays in ...
4
votes
1answer
45 views

Algorithm does not converge in R

I am doing a logistic regression in R, where I am modeling how potholes and weather correlate to accidents. When I run a logistic regression, I get the message "Algorithm does not converge" The ...
0
votes
0answers
45 views

Regression Analysis and Optimization in R

I have the following table: ...
0
votes
2answers
40 views

How to modify variables to be significant in logistic regression?

I am running a logistic regression analysis in a particular software. My objective is to study the behavior of the software with significant variables. However, with the data I have, there are no ...
2
votes
0answers
58 views

Is regularization of a linear model really needed?

I'm going to do linear regression on a data set with 60k observations, each with 120 features. The way I see it, there is no why in the world that with more then 50 samples per dimension, a linear low ...
0
votes
0answers
11 views

Should the rivals models include the same number of observed variables?

The question is about comparing models that include different number of observed variables. For example consider I have an 80-items questionnaire and I want to do confirmatory factor analysis (CFA) in ...
1
vote
0answers
18 views

Categorical Variables - Factor Reduction - Can I use the dependent variable?

I am working on a basic fraud detection model. I have about 10 independent features and I am trying to predict if a given transaction is genuine or fraud. Most of the features are categorical and each ...
1
vote
1answer
23 views

Confusing definition of Potts model

I came across some Markov random field models and noticed something that didn't make sense to me. One of these models for a set of latent variables $\{z_{i}\}$ is the following: $$p(z) = ...
5
votes
1answer
72 views

Assuming a probability density for MLE to do model selection

Motivation: I am trying to use Akaike Information Criterion to assess model ranking and over-fitting risk for a set of nonlinear models. I am an electrical engineer with no formal statistical training ...
0
votes
0answers
40 views

Model averaging with MuMIn. What's the mean of pvalue?

the summary() of a model.avg made with MuMIn in R, give a lot of interesting results, in particular model averaged coefficients (estimate, standard error, adjusted standard error and a z value with a ...
2
votes
0answers
24 views

Is there a simple model that assumes a negative relationship between the mean and variance of response?

Poisson and Negative Binomial models assume that the variance of response is greater or equal to the mean of response. Is there a simple model where that assumption is reversed, i.e. variance goes ...
5
votes
1answer
140 views

Are normally distributed residuals not necessarily homoskedastic?

Let's say I've ran a linear regression and I'm checking the model diagnostics. I made a histogram of the residuals and they appear more or less normally distributed as below. I thought for a long ...
0
votes
0answers
34 views

Can we use the AIC values to compare a hurdle poisson model to a multinomial logit model?

I estimated two different models using an SP survey: Hurdle poisson and multinomial logit with 5 alternatives. My dependent variable is the number of weekly trips (0,1,2,3,4,5 trips) that students ...
0
votes
0answers
25 views

Hypergeometric distribution and its continuous counterpart

I'm modeling an urn without replacement. I'm drawing $m$ balls from an urn containing $p_w$ white balls and $p-p_w$ black balls and I want to know what is the probability that I draw at least $m_w$ ...
1
vote
0answers
8 views

AIS data modelling! [closed]

I am interested in exhaust emission modelling with the use data received from vessels equipped with the Automatic Identification System (AIS). Are there any resources developed so far in relation to ...
1
vote
0answers
21 views

Is there a list/catalog of types of regression to choose from?

In my practice, I often find out that after the basic assumptions for linear regression are not met, I have to discover a new type of regression I should use. For example, in the past, I came across ...
3
votes
2answers
88 views

Function Approximation vs. Regression

Some background before I state the questions: I have a $d$-dimensional random vector $X=(X_1,\ldots,X_n)$ and a function $f:\mathbb{R}^d\rightarrow\mathbb{R}$. Ultimately my goal is to understand $f$ ...