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 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 ...
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29 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. ...
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10 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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22 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 ...
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1answer
22 views

Costs in a game [on hold]

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, ...
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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 ...
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1answer
28 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 ...
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66 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 ...
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0answers
11 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 ...
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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.
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2answers
79 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 ...
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1answer
48 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 ...
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1answer
49 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 ...
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9 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 ...
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3answers
33 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 ...
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1answer
94 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 ...
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2answers
84 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 ...
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1answer
47 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 ...
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24 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 ...
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15 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 ...
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1answer
27 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 ...
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15 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). ...
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19 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, ...
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1answer
28 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 ...
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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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9 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 ...
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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 ...
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24 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 ...
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2answers
99 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 ...
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0answers
11 views

Comparing the slopes from two models

I'm working on investigating the results of two models of a dataset. The dataset has several thousand data points (representing daily measurements over the course of several years). The data have a ...
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2answers
44 views

Predictive Model - Increase Pediction Accuracy for Less Likely Events

I am trying to build a model that predicts the which binary category a respondent belongs to (0 or 1). I have demographic variables (all categorical) and a few 10 point questions. I have built a few ...
3
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2answers
42 views

What is the proper name of a model that takes as input the output of another model?

Thanks in advance for the help. I am writing a paper and for the life of me can't remember the proper term for a model that works as follows. ...
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1answer
38 views

How is data generated in the Bayesian framework and what is the nature on the parameter that generates the data?

I was trying to re-learn Bayesian statistics (every time I thought I finally got it, something else pops out that I didn't consider earlier....) but it wasn't clear (to me) what the data generation ...
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8 views

Finding significant values in my tree-like distributed data

i have a question about my data. Let me first describe what i know about it. I know that the number of data points I have is large (200-1000) I know each data point value is greater or equal to 0 I ...
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11 views

Creating a model for a webshop

I'm going to create a Multi-armed bandit algorithm to handle recommendations for a large scale webshop. I'm going to use Thompson sampling (http://en.wikipedia.org/wiki/Thompson_sampling) and would ...
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1answer
55 views

Finding significant predictors of psychiatric readmissions

The set of data I am working contains nearly 17,000 independent spells (each spell consists of a number of hospital episodes) each belonging to a unique patient ID. I have spent a very long time ...
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23 views

Logistic regression and IV that depends on another IV value

I am modeling the effect of aspects of house change and marital status change on a (binomial) DV. Each observation in my data is a 3-year period in someone's life. Thus, for family change, I have a ...
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0answers
25 views

Topic models (LDA), word cooccurances in documents?

I have read on papers that Latent Dirichlet Allocation (LDA) works by identifying word cooccurances in documents. What is confusing me is since LDA uses bag-of-words approach for document ...
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1answer
40 views

Variation on the urn problem and frequency distribution

I have $6$ machines each producing different coloured balls. The balls are mixed together in a large vessel. Groups of $6$ balls are extracted at random for packing. Each pack will therefore have a ...
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2answers
82 views

Statistical significance versus sample size [duplicate]

Statistical significance of a (variable in a) model grows with sample size. Citing Gilbert (1986): If one uses test statistics with constant size (i.e. a constant degree of confidence), almost any ...
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0answers
35 views

Generalized Linear regression Model in R [closed]

I run the GLM model on insurance dataset, I got the following script at the end of execution. ...
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3answers
66 views

How to take advantage of multiples series with the same behaviour for forecasting?

I'm quite new to statistics and forecasting, and I have to build a model to forecast monthly sales of different related products in a bunch of cities. Seasonal ARIMA seams to be a good model for ...
3
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0answers
28 views

Prior on sum of bernoulli variables

I wish to model my data as follows: $$ y\sim\mathcal{N}(X\beta,\sigma^2)\\ \beta_i\sim\mathcal{N}(5,1)^{z_i}\mathcal{N}(0,1)^{1-z_i}\\ z_i\sim logit(\gamma_i)\\ ...
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0answers
25 views

Deterministic Model and Stochastic Model

Deterministic model involves no randomness, where as stochastic model involves randomness. An example of deterministic model is: return of $5$years of investment with an annual interest of $7$% . An ...
2
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1answer
38 views

Is testing predictors separately theoretically sound?

I am running a regression analysis to understand the effect of several IVs on the transport mode choice of questionnaire respondents. My sample of respondents is of 100, and I have more than 10 ...
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0answers
21 views

How to test if model can explain data?

I have a purely deterministic system-theoretic background, so please bear with me if this is elementary. The question is related to: How to test whether a series data follow Ornstein-Uhlenbeck ...
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0answers
25 views

How do you approach transformations when modeling?

I'm working with a simple univariate dataset and I've built several models for it. Some I think are fairly decent given that datas structure. In order to get a decent model I had to do some ...
2
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3answers
299 views

Why “modeling volatility” is not an oxymoron?

Firstly, I'm sorry, if my question will come across as simple or even naive, but I have no formal background in statistics and I'm trying my best to learn it as much as I can, among other areas. My ...
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2answers
46 views

Proportion of variance in dependent variable accounted for by predictors in a mixed effects model

Let say I've ran this linear regression: lm_mtcars <- lm(mpg ~ wt + vs, mtcars) I can use anova() to see the amount of ...
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29 views

Optimal number of workers for small business model

I'm new here. I formed an analytical model of a small scale business where the expense can be defined as $C_L=(1-T).(1-1/n)$ and production rate can be defined as $R=1/(1-T+T/n)$. Where ...