A statistical model is a formalization of relationships between variables in the form of mathematical equations. The model is statistical as the variables are not deterministically but stochastically related.

learn more… | top users | synonyms

0
votes
0answers
6 views

Continuous low truncated response in regression

I can't find a clear answer on how to model a regression with a low bounded response. The tipical case is with response variables that can take only positive results. Poisson and negative binomial ...
0
votes
0answers
17 views

statistical modelling for imbalanced data

I am dealing with a binary response (good/ bad) type data set of size 2153, which reflects a dependent variable. Out of these, only 67 are in favor of "bad" and the remaining are of "good". Also, i ...
0
votes
0answers
23 views

Linear Probability Model Construction

I am dealing with a questionnaire consisting of 15 questions. I performed a factor analysis to all the questions and it has given three factors each consisting of 5, 7 and 3 numbers of questions ...
0
votes
0answers
21 views

Is there any argument in GAM function (R software) to tell the model which variables are categorical or continuous? [closed]

I want to fit a species distribution model for some bat species using a generalized additive model (GAM). I have 124 presence points and I generated 10000 random absence points. I also have 18 ...
3
votes
1answer
36 views

Help constructing a simple regression model with a breakpoint

This is related to my questions here and here. I am still struggling with my model, so I am taking it back to basics. My assertion is simple, I believe that watershed runoff will have a different ...
2
votes
2answers
46 views

Combination Forecast - Which models to pick?

Combination Forecasting can be produced by simply averaging different forecasts or employing more complex techniques (see Makridakis, 1989; De Gooijer and Hyndman, 2006; Goodwin, 2009; Pesaran and ...
0
votes
0answers
19 views

Can I mix data and do a regression and/or can I sum multiple regression formulas for a 'master' formula? [closed]

I need to make a formula for the line of best fit (trendline) for multiple regressions, (and I'm working with a college-level statistics knowledge so please forgive my ignorance). The data I will be ...
3
votes
1answer
61 views
+50

Maximum number of alternatives in a discrete choice model

We are modeling a discrete choice scenario, with alternative-specific coefficients. We also break the assumption of independence of irrelevant alternatives. To model this, we are using an ...
0
votes
0answers
28 views

Project help [undergraduate] [R]

I have to do a project for my statistics course, which I have now finished but would very much appreciate some insight and help as I've never done anything like that before. I was given data on ...
0
votes
0answers
23 views

Optimal model/statistical test for my design?

I have a design with 1 between-subject factor and 2 within-subject factors as independent variables and 5 dependent variables (longitudinal accelartion, lateral acc., response time, first conscious ...
2
votes
0answers
13 views

A control variable that forms part of the definition of the dependent variable: Drop it or transform it?

My aim is to analyze, using OLS, how Y (firms' benefits) depend on some factors. To normalize Y, I divide it by firms' size (S). Therefefore, my dependent variable is Y/S. To know how size affect ...
1
vote
1answer
44 views

How to decide whether a variable belongs to a linear model?

I have a set of inputs $x$ and noisy outputs $y$. I think that either $$y = a_0 + a_1 x$$ or $$y = a_0 + a_1 x + a_2 x^2.$$ How can I determine which model was more likely to have generated the data? ...
0
votes
0answers
15 views

Library for using LASSO to tune parameters of arbitrary model?

I typically see LASSO applied to the question of finding coefficients of a linear model. I'm looking for a library/tool that performs LASSO with an aim to tune the parameters of an arbitrary ...
1
vote
1answer
32 views

Does AIC require the residuals of the model to be normally distributed?

Does AIC require the residuals of the models to be compared to be normally distributed?
0
votes
0answers
21 views

Determining if data follows a Sigmoid function given data that does

I am essentially trying to determine if some gene expression data I have follows as circadian pattern. Currently I have gene expression data for genes know to follow a circadian pattern. I want to ...
0
votes
0answers
16 views

hedonic model: estimating coefficients for variables not used in the regression

I'm trying to estimate the value of a real estate upon its characteristics. To do so, I'm using the Hedonic Model and I'm doing the regression using ...
0
votes
1answer
15 views

Nonlinear Gompertz model

Is it possible to use Gompertz to model a non time series data? i.e. change t (time) into x (independent variables). Data does not involve growth or survival.
4
votes
1answer
63 views

What distribution is appropriate for modeling internet forum posting?

Internet message boards, such as 4chan, consist of a series of "threads" to which people post replies. One observes that most threads receive zero or one reply, a few receive 2 or more, and only the ...
0
votes
0answers
25 views

choosing between non-linear/logistic regression models

We are fitting regression models to dose-reponse data obtained from drug assays. Where a drug has an effect on cell viability, there is often a sigmoidal relationship between the proportion of viable ...
0
votes
0answers
19 views

Fitting a model while adjusting for some variable

I plan to figure out the effect of variable X on variable Y. I have time series data for both X and Y and a simple regression model should do the job. Unfortunately the variable Y is also affected by ...
0
votes
1answer
16 views

Model for probability of N autocorrelated events

Say we have $N$ birds, $r$ is the probability that one bird sings. What is the probability $p$ that any of $N$ birds sings? If we assume independence, there is a simple model describing the ...
0
votes
0answers
58 views

Setting up a aov model in R for a variant of a 2-way nested ANOVA

I have a rather complex experiment i need to analyse The experiment was an infection trial, where the effect of two different infection methods ("inoculation" and "in-contact infection") were ...
1
vote
1answer
78 views

Creating a disease severity score/index

I am trying to design a numerical scale which would describe the severity of a certain disease (in this particular case anaphylaxis). I have a set of clinical symptoms and a database of patients who ...
0
votes
0answers
16 views

How to get standard errors for parameter estimates from full model-averaged coefficients (with shrinkage) [migrated]

I'm using MuMIn to calculate parameter estimates in a model averaging procedure. Right now I want to compare parameter estimates from conditional vs shrinkage. I want to compare both parameter ...
0
votes
1answer
48 views

Model Selection and RFE using caret

I'm faced with a high dimensional (samples=148, features=20000), supervised binary classification problem. Which I would like to approach with an ensemble of classifiers, that will classify using a ...
1
vote
0answers
77 views

Statistical model Regression

A statistical model is often defined as a set of probability distributions on a random variable of interest, indexed by a parameter $\theta$; hence, for continuous random variables equivalently as a ...
0
votes
0answers
8 views

Heterogeneity test for risk factor association (hazard ratio/coefficient) in two or more different survival outcomes

Suppose I'm interested in comparing smoking category (current, ex, non-smoker) as a risk factor in two or more different but similar (considerably exclusive) outcomes, say lung cancer of different ...
0
votes
1answer
76 views

Optimizing parameter estimates by minimizing chi^2 in iterative procedure

I need to minimize my Chi^2 (bottom-left in figure 1) by adjusting parameter-values in a MLE-procedure (or something alike). The chi^2 (red) is a goodness-of-fit measure. It expresses how well the ...
0
votes
1answer
80 views

Mathematical Modeling and Statistical Modeling

What is the difference between mathematical modeling and statistical modeling? I only know that a mathematical model is deterministic while a statistical model is stochastic. Is that all to answer ...
0
votes
0answers
13 views

Binary outcome and correlated predictors

I have binary outcome variable (infection yes/no), two types of predictors, correlated (CRP 1, CRP 2 and CRP 3) which can be numeric or binary (it's pretty same to me) and uncorrelated predictors ...
0
votes
1answer
31 views

No ARIMA, No GARCH, which model?

I am trying to fit a model for a data set. The acf and pacf, after differentiating the data are: The acf shows that the returns appeared to be random. According to them seems like the ARIMA model ...
9
votes
1answer
115 views

Additive Error or Multiplicative Error?

I'm relatively new to statistics and would appreciate help understanding this better. In my field there is a commonly used model of the form: $$P_t = P_o(V_t)^\alpha$$ When people fit the model to ...
0
votes
0answers
17 views

What is Tau and Omega in the Black Litterman model?

I'm looking into the BL model when it comes to portfolio optimization, and I'm having a hard time trying to understand each one. I've read on several papers that Omega is the covariance matrix, but I ...
1
vote
1answer
137 views

RMSEP vs RMSECV vs RMSEC vs RMSEE

I am getting real confused now, What is the difference between, RMSEP (Root Mean Square Error of Prediction), RMSECV(Root Mean Square Error of Cross Validation), RMSEC (Root Mean Square of ...
0
votes
0answers
19 views

How Do I know if my nonlinear model properly fit my data?

My observational data shows kind of a power shape and I have fitted my data using a power model (Y=a*X^b). I would like to know if the fitted model represent my data at the significance level of 5%. ...
1
vote
1answer
50 views

Difference between “Design based approach” and “Model based approach”?

In a pdf file, i found the following thing which i have not understood at all. ‐ one view (e.g., Heckman, 2008): causality is model‐based: causality only exists within the framework of a theory ...
0
votes
0answers
3 views

How do I evaluate if an SIR model is consistent with a new-product diffusion model?

Background Susceptible-Infected-Recovered (SIR) models are used in epidemiology to determine the spread of disease. (link) The Bass models for new product diffusion are textbook in sales. (link) ...
0
votes
0answers
19 views

Model and Modeling

model and modeling seem identical to me. Aren't those really same ? (or is there any flaws so that they are two different tags.) And in model tag, it is written ...
0
votes
0answers
21 views

Model Selection Problem

I am asking if there already exist approaches and researches on the following topic. Imagine there are 10 stores and in 3 stores labeled training data was available, so I built 3 classification ...
2
votes
0answers
21 views

prior for integer-valued random variable taking values 1 or greater

In my model I have an integer-valued random variable which should only take values one or greater. I would like to specify an appropriate prior for this which has most of the mass say around 1 to 5 ...
2
votes
1answer
146 views

Logistic regression shows a significant predictor, but a simpler model makes the same prediction

I am currently puzzled by the classification table SPSS produces for logistic regressions (procedure LOGISTIC REGRESSION). I used the block function for that ...
0
votes
2answers
65 views

Model for exponential decay with lots of zeros

I am trying to test for the effect of a treatment on a response variable. The response variable decays over time in what I believe is an exponential way. The measurement doesn't go below zero, so ...
0
votes
0answers
3 views

How to interpret/detect interactions with proportional effects

Assume I have an experiment with 2X2 factors. Let's name the first factor F1 with the levels ...
0
votes
0answers
44 views

R: Interpreting mlogit coefficients

Edit: The subset of the dataframe I provided way giving a different error, so I've replaced the pastebin entry with the full data frame. Here's the top of the data frame, then read into mlogit format ...
3
votes
2answers
131 views

Should I remove non-significant variables from my regression model

I have run a multiple linear regression using stepwise regression to select the best model, however the best model returned has a non-significant variable. When I remove this the AIC value goes up ...
0
votes
0answers
15 views

Can linear regression model variables be constantly looped in simulations to find the perfect model? [duplicate]

Say that I have a very large dataset and have a fairly large amount of variables, let's say 30. Since I don't know which variables matter and are good predictors for regression, I construct a for-loop ...
1
vote
0answers
9 views

Parameter Tying: Using observations of one category to lift estimates of baseline ability

I am trying to model an individuals' ability to perform one of several similar tasks. We would like each individual's performance to reflect three factors: the mean ability of the general population, ...
2
votes
0answers
17 views

Multilevel regression: question about notation

I have some difficulties in understanding the notation of multilevel regression models. Let's consider, for example, a varying intercept and varying slope model with just one level-I predictor. We ...
0
votes
0answers
20 views

Split-plot design with subsampling

Do split-plot designs allow sub-plot replicates or would they technically be pseudoreplication? For example, if fertilizer were the whole plot treatment and two types were applied to four fields (so ...
0
votes
1answer
35 views

How to prove absolute lack of correlation

I have a huge dataset of 17 variables. I intended to use 15 of those to predict the 17th, and I could not find any model (ANN) to do so. I know that one of those variables definitely predicts the ...