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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4
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1answer
118 views

Different shapes of an ROC curve

What are the possible shapes of an ROC curve? Is it necessary for an ROC curve to be shaped like a normal distribution curve? Can we regard the following two curves as ROC with the area under the ...
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0answers
11 views

Generating markovian paths

given a 4 by 4 transition matrix P, I want to simulate m Markovian paths of length n I created this function can anyone tell me if this is is correct ? ...
1
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1answer
46 views

Statistical models for Obesity data

I have a data on obesity status of women in a country. This data is based on across sectional study. Now I want to find the determinants of obesity among the women. Here dependent variable is binary ...
3
votes
1answer
26 views

Studying complex systems (complexity)

Complex socio-technical systems is one my research interests. Since I plan to further study such systems and related phenomena, I've done a bit of reading and ran across various books, such as Bar-Yam ...
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0answers
9 views

Cox MSM or Recurring Event?

I have dataset containing clients' history of active and inactive periods (if there is any) up to present day. Also lots of info as time dependent covariates. I want to model this as MSM, where both ...
3
votes
1answer
34 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 ...
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0answers
18 views

How to get the y variables that were not deleted due to missingness? [closed]

I have used a logistic regression model to predict some y, given some xes. I created the model using the following syntax: ...
0
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0answers
25 views

What do you do with outliers when developing statistical models?

I am a beginner so I have an extremely tough time dealing with outliers. I wanted to ask the community to help me understand rule of thumbs or anythng that would help me deal with these questions ...
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0answers
11 views

How to label observations based on latent class analysis

I perform a latent class analysis to a dataset of binary variables with ...
0
votes
1answer
16 views

Designing an experiment for a marketing campaign using Incremental Response Modelling

I have the following hypothetical question, can anyone provide some clarification? I'm looking at designing an experiment or modelling what steps can be taken to maximise the Net Incremental Revenue ...
1
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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
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1answer
29 views

Methods for determining cause and effect relationships between time lagged variables?

As you know, one can use regression for inference to learn which variables correlate with a response variable if the input predictors and the response share the same time frame. Let's say a predictor ...
2
votes
6answers
90 views

How to prevent collinearity?

Ieno & Zuur 2015 describe a number of causes of collinearity among explanatory variables entered into a linear regression. One of these causes is what they call a 'data collection' cause. They ...
3
votes
2answers
59 views

Are (some) time dummies redundant if another variable controls for a part of the sample period?

For an OLS regression, on the one hand, I have a dummy variable for each sample year (from 2000 to 2012). On the other hand, I have a binary variable that is 1 if observations refer to a concrete part ...
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0answers
6 views

contrast coding of nominal predictor variables by mean of the response

What is your opinion of contrast coding of nominal predictor variables by mean of the response variable? Let's say one has a data set with nominal values of sunshine (cloudy, mostly cloudy, mostly ...
1
vote
1answer
44 views

Multilevel Modeling in stata

I would like to make a model that calculates the probability of disease. Range of variables are following: disease ~ (0, 1); score ~ (1-10); test ~ (0-30) Large values of test and score indicates that ...
1
vote
0answers
45 views

How to statistically validate a Framework?

I am creating a Framework, that has independent variables A and B. It has dependent variables C, D, E. F, G, as shown in the diagram. Framework I understand that only after validating a Framework we ...
5
votes
1answer
35 views

Pitfalls in Fitting Nonlinear Models by Transforming to Linearity

Some nonlinear models can be transform to linear models. My understanding is that there might be one-to-one relationship between the estimates of nonlinear model and its linear model form but their ...
0
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0answers
17 views

Qualitative and quantitative effects

I am studying two different types of fees in contracts (both types are usually present in any contract). Each type can be x (fixed) or v (variable). Since no contract is mv (the first (second) letter ...
0
votes
1answer
31 views

Model comparison between glm (with Firth correction), random Forest, penalised SVM

I am currently developing three models to classify features of gene sites. I was using glm (with Firth correction), random Forest and SVM to build the models and I used forward and backward ...
2
votes
1answer
35 views

Comparing linear and nonlinear models

Is it possible to compare between these two types of model? I have a set of data that involves 6 independent variables and 1 dependent variable. It is based of a questionnaire for social science ...
0
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0answers
7 views

Approach to plotting a shock model with a Poisson parameter?

I know the shock model is defined as: $$ A(t) = \sum_{i=1}^{N(t)} A0\exp[-a(t-sn)] $$ where: $a = 0.5$ N(t) is Poisson ~ lambda, and lambda = 4 $t = 5$ $A0$ is given its own uniform ...
3
votes
0answers
42 views

Modelling flight delays with negative values

Modelling flight delays with negative values I am working on a model to predict whether a flight will be delayed. The data consists of some explanatory variables for flights from a specific airport. ...
1
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0answers
13 views

Prior for a linear transformation matrix: Matrix Normal Distribution

I have been trying to derive some conditional distribution for parameters of a linear transformation (represented as a matrix) and I had a lot of help on this thread yesterday. However, I realised I ...
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0answers
24 views

Device Comparision: Correlated or uncorrelated measurements

Background: I want to compare two devices measuring a certain characteristic on a subject. Thereto, each subject is measured once with device A and once with device B. It needs to be assumed that ...
0
votes
1answer
22 views

Costs in a game [closed]

I am trying to find the best model to solve the following problem: I have various players in a network. I have to visit all these players in the network. The players are visited in an optimal route, ...
0
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0answers
10 views

Comparing the impact of 2 independent variables on the dependent

I'm using a predictive modelling technique which has 2 parameters. I've performed a sweep of values for each of these 2 parameters, running each permutation of parameters 30 times as the technique is ...
2
votes
1answer
31 views

Choosing among PDFs

This is a pretty broad question. I just learned that two random variables can have the same moments but different PDFs. Take $\mathbb{E}[X_i] = \mu$ and $\mathbb{Var}[X_i] = \sigma^2$. Since there are ...
6
votes
1answer
92 views

Parameters vs latent variables

I have asked about this before and have really been struggling with identifying what makes a model parameter and what makes it a latent variable. So looking at various threads on this topic on this ...
1
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0answers
15 views

Zero-inflated negative binomial regression: 0 probability of a count greater than 0

Zero-inflated negative binomial regression assumes 0s are generated by two processes: a group whose counts are generated by a negative binomial regression and a group who have a "0 probability of a ...
0
votes
0answers
13 views

Help forming a VAR model

Can anyone help me for a very basic VAR model for regressing Inflation (CPI first difference) on energy prices and money supply. any suggestions be appreciated.
3
votes
2answers
98 views

Justification for using a zero-inflated negative binomial regression

I'm trying to describe in words why I used a zero-inflated negative binomial regression instead of an negative binomial regression: To model my data I used a negative binomial regression. However, as ...
2
votes
1answer
55 views

How is ABC more computationally efficient than exact Bayesian Computation for parameter estimation in dynamical systems (ODE) models?

Approximate Bayesian Computation has been suggested as an approach to parameter estimation for computationally intensive simulations, most commonly in population genetics, but also in dynamical ...
1
vote
1answer
61 views

Larger p-values but less misclassification error in Logistic Regression

I was doing logistic regression in R on 'Smarket' data set available in the ISLR library. Since correlation between variables were less, I used all variables in my model and I was getting the ...
0
votes
0answers
12 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
3answers
38 views

How to predict categorical reponse?

I am trying to predict categorical response by using several categorical variables and quantitative variables? I tried linear regression model in R, but I don't think it works well as the response is ...
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 ...
2
votes
2answers
91 views

Can my Bayesian prior reflect what the data should say rather than what it could say?

Can my Bayesian prior reflect what the data should say rather than what it could say? For example, assume I collect data where $Y_i$ is whether or not student $i$ passed the test and $X_i$ is whether ...
0
votes
1answer
54 views

Why would the residuals from these two models result in the wrong AIC being calculated?

We run two linear regressions, Model 1 and Model 2. The residuals from these two models are plotted against the predicted values. If I understand correctly, the AIC from these two models would be ...
0
votes
0answers
26 views

How to incorporate exchange rates in a multi-country econometric model?

I have for 12 countries variables related to the trade of one specific commodity (production, consumption, import, export, trade costs, and international prices). The data range is 2000-2013 with a ...
1
vote
0answers
16 views

Are wrong standard errors a problem if using information theoretic model selection?

In linear regression, if the assumptions of normally distributed residuals and homogenous residuals are broken, incorrect standard errors can be calculated. This can lead to some predictors appearing ...
0
votes
1answer
38 views

Parameter Estimation vs Inference Error

I am having trouble reconciling (or maybe even understanding properly) the following issues: We have a dataset. We hypothesize a functional form for probability density. Then we estimate the ...
0
votes
1answer
24 views

Structural Equation Model - Construct Operationalization

I'm building a Structural Equation Model and I'm trying to operationalize my Dependent Variable, the construct of "Investor Behaviour" (= whether or not an investor is willing to invest in a startup). ...
3
votes
0answers
23 views

Modelling of probabilistic vs deterministic systems

The learning problem in Statistical Learning Theory is defined as: $$ R(f) = \int_{X,Y} L(y, f(x))P(x,y)\mathrm{d}x\mathrm{d}y $$ where $R(f)$ is the expected risk $L$ is the loss function $P(x, ...
0
votes
1answer
32 views

Parametric distribution for time to event data - where event is 'uncertain'

Is there a canonical approach to deal with the modeling of time to event ($A$) where $P(A) \ne 1$. For instance, assume the marriage rate is 50%. The study is a set of times (ages) until marriage ...
0
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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 ...
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0answers
10 views

What models allow the study of the relation between a set of response variables and a set of covariates?

A first technique that comes to mind is Canonical Correlation Analysis. Bayesian Networks and other graphical models, I guess, can also be used to analyse such things. Any else that I should be aware ...
0
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0answers
15 views

Suggestions of a statistical model with IID data and latent variables

While this might be an unusual request, I am looking for a statistical model with certain properties to test my numerical method on and thought I might ask here. The model ought to have the following ...
1
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0answers
29 views

How to deal with many NAs in dataset when comparing several models?

My task is to compare different logistic regression models to examine which theory better explains the data we have collected. In doing so, I have a set of IVs with a considerable amount of missing ...
5
votes
2answers
125 views

Assumptions behind multinomial logistic regression

What are the proper assumptions behind multinomial logistic regression? And what are the best tests to satisfy these assumptions in any statistical software? What are other suitable models, if those ...