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

More comprehensive summary() function in R? [migrated]

I was wondering if there was a more comprehensive summary() function in R that perhaps includes more model metrics such as confidence intervals around the estimates maybe log-likelihood, AIC, BIC ...
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1answer
28 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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0answers
18 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
32 views

What statistical models should I use for this data?

I had evaluated the effect of daily pollution on daily death with Poisson time series models. As I have individual level ID and exposure data, I wish to investigate this in more detail. The aim is ...
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15 views

Model Suggestion for Temporarily Broken Device

I am looking for some modelling suggestions for the following scenario: Let's say there is a device making some regular measurements of a neutral phenomenon with almost perfect measurements (no ...
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0answers
22 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
34 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
47 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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34 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
63 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
27 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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20 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
36 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
19 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
22 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
289 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
38 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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0answers
22 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 ...
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0answers
31 views

Machine learning approach for modeling stochastic observations [closed]

Let X={xi, i=1:N}, where xi ranges within [a,b], and the distribution of xi is bimodal, but noisy. Now we have several observed instances of X: X’={xi’, i=1:N}, X’’={xi’’, i=1:N} etc, where {xi’} ...
3
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0answers
41 views

Is approximating used car prices with deep learning over-engineering?

I am supposed to build an application that uses deep learning to approximate prices of used cars. My concern is that deep learning is too general of a tool for the problem at hand. I am going to use ...
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1answer
30 views

AUC and Balanced accuracy in R Modelling

Can someone please explain the difference between AUC(Area under curve) and balanced accuracy in R? For eg: In decision tree modelling I got the, AUC : 0.91 balanced accuracy : 0.72 please explain ...
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0answers
11 views

Help with fixed effects regression: adding variables turns regressors insignificant [duplicate]

after composing my model for estimating the relationship between the old-age dependency ratio (share of elderly, i.e. 65+ who are dependent on the working population) and GDP per capita, I stumbled ...
3
votes
1answer
79 views

How to - regression of a noisy titration curve?

I'd appreciate advice on the correct statistical method to analyse a dataset - Dataset is basically a titration curve consisting of [0.5, 1, 2, 3, 4, 5, 6] pg of starting material and 8 replicates ...
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3answers
43 views

Test for effect of groups in a mixed effects model

This model is a simple linear regression: mtcars_lm <- lm(mpg ~ wt, mtcars) And this model adds cyl as a random effect: ...
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0answers
21 views

Odds, log odds and odds ratios [duplicate]

I'm trying to get an understanding of what these three statistics are in the context of logistic regression: Odds Log odds Odds ratios Can anyone provide a short, intuitive summary of these three ...
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2answers
51 views

Finding the best fit continuous distribution

I'm an applied math graduate student with little background in statistics. I'm currently dealing with the following problem: I have a set of samples that I'm trying to fit a (continuous) distribution ...
4
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3answers
102 views

Decompose a time series into superposition of step functions?

Background I have time series data comprising hourly observations of a sensor's readings over a period of almost a year. The sensor records an environment whose baseline measurements should have ...
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1answer
23 views

Complex level 1 variance mixed effects models in R

Take this mixed effects model in R: $y_i = \beta_0 + \beta_1X_{ij} + u_{j} + e_{ij}$ where $u$ is a random effect (level 2 residual) with groups $j$. It is possible to allow the variance of $e_{ij}$ ...
3
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1answer
63 views

Find the mode of a probability distribution function

I am trying the find mode of a probability distribution function given by \begin{equation} g(x/\alpha,\beta,\sigma)=\frac{1}{\Gamma \left( \alpha ...
0
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1answer
40 views

Time varying Gaussian distribution

Does there exist time varying Gaussian model? To be specific, a 2D Gaussian model whose mean and covariance matrix is varying with another parameter called time. The $\mu$(mean) and ...
6
votes
1answer
39 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). ...
2
votes
1answer
16 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 ...
1
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1answer
70 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
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1answer
46 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 ...
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1answer
12 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 ...
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0answers
21 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
77 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
146 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
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3answers
39 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
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1answer
35 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
172 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 ...
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votes
1answer
50 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
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1answer
22 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
28 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
40 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 ...
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0answers
26 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
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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
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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
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0answers
29 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
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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 ...