0
votes
1answer
6 views

Would PCA work for boolean data types?

I want to reduce the dimensionality of higher order systems based and capture most of the covariance on a preferably 2 dimensional or 1 dimensional field. I understand this can be done via principal ...
0
votes
0answers
7 views

Dummy coding , Effects coding , is there somthing called "Averaging coding or mean coding in Regression model?

When we perform a regression analysis with categorical predictors, we can use (1, 0) " Dummy coding ", the coefficients in this case represent the deviation of the groups' means from the mean of ...
0
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0answers
3 views

GAM : choosing the a function for each predictor

When using GAM methods for prediction: y = af(x1) + bf(x2) + cf(x3)... Where x1,x2.. are predictors / features used in the data. The gam package in R helps us find the best functions and the ...
0
votes
3answers
32 views

explanation of MCMC and bayesian estimation

I have some questions about bayesian methods. First of all I have a set of iid observations, calling $y_1,\ldots,y_n$ that come from a certain distribution $f$ with unknown parameters(for example ...
0
votes
2answers
70 views

Mixing probabilities in mixture models using EM

Assume we want to estimate the mixing probabilities ($\pi_{k}$) for each member distribution in the mixture model. We know that $\sum_{m}^{K}\pi_{m}=1$, so we can formulate the optimization problem ...
0
votes
1answer
12 views

Goodness of fit in R

I need to check for goodness of fit using K-S test in R. I have a dataset (containing 50 data points) and a non-standard continuous probability distribution. Should I peform a one-sample or two-sample ...
0
votes
1answer
21 views

Regression without intercept: deriving $\hat{\beta}_1$ in least squares (no matrices)

In An Introduction to Statistical Learning (James et al.), in section 3.7 exercise 5, it states that the formula for $\hat{\beta}_1$ assuming linear regression without an intercept is $$\hat{\beta}_1 ...
0
votes
0answers
20 views

Sample Size for Comparing two Measuring Tools

Let's say I have two devices that measure the same thing: the thickness of an object. Knowing that device 2 is more accurate and precise, I wish to see how accurate device 1 (how close do the readings ...
0
votes
1answer
92 views

Comparing classification algorithms using cross validation and caret's train

I am having issues understanding some concepts of algorithm comparison/parameter optimization/cross-validation in R Let's say I want to compare two classification algorithms, such as Random Forests ...
1
vote
0answers
3 views

How to reconstruct moments from aggregated data?

Let $X_{1\ldots n}$ be a stochastic variable that is log-normal distributed, with parameters $\mu$ and $\sigma$. Now suppose all $X_i$ are aggregated into $Y_{1\ldots m}$ where $Y_1$ is the mean of ...
0
votes
0answers
5 views

Turkey Multiple Comparision

I used GLM to run a Turnkey multiple comparison. The p-value of model 0.6483; so the model is not significant. The differences of pairs show there is not significant difference of means between ...
2
votes
1answer
88 views
+50

Fourier transform and the multivariate normal

I am wondering about how to specify multivariate normal distributions for vectors that have undergone a Fourier transform. For instance: Say we have the mean vector $\boldsymbol{\mu}$ and covariance ...
6
votes
3answers
1k views

Case-mix adjustment versus risk adjustment, what are their differences in practice and objective?

I have encountered in swathes of medical literature the use of the terms "case-mix" and "risk" adjustment without any citations or explanations of their exact usage and motivation from a modeling ...
1
vote
0answers
26 views

Why areny my 95% confidence intervals symetrical around the mean?

I am trying to calculate the 95% confidence intervals around a mean value using a bootstrap procedure, from the mosaic pacakge, to deal with some assumption issues in my data. The code below seems to ...
2
votes
1answer
114 views

Minimizing KL divergence from a given distribution, according to a graph

Given $n$ discrete random variables $X_1,...,X_n$, a distribution $p$ on $X=(X_1,...,X_d)$ and a DAG (Directed Acyclic Graph) $G$ on $\{1,...,d\}$, which is the distribution $q$ factorizing with $G$ ...
3
votes
3answers
31 views

Statistical test for increasing incidence of a rare event

I have following simulated data of 2500 persons regarding the incidence of a rare disease over 20 years ...
0
votes
0answers
7 views

Representation of misspelled words for neural network?

While thinking about a neural network based spellchecker, I was thinking about word embedding not being able to represent any "unique" (misspelled) words that the model haven't seen before. I tried ...
0
votes
2answers
15 views

Simulate ARIMA Model in R using same starting values as original time series?

I have built an ARIMA model in R with the forecast package's auto.arima() function. I want to simulate the ARIMA model with the same starting values as the original time series. For example, if my ...
1
vote
1answer
485 views

Doing CFA on a known theoretical model, but having problems with convergent and discriminant validity

I did a survey based on the UTAUT model / theory with standard questions used. The N was 150. I am trying to validate the model for my survey with structural equation modeling (via the AMOS software). ...
4
votes
2answers
45 views

Best way to test that one mean is greater than all the others

Suppose I have $k$ samples of different sizes, each of a different univariate variable. I want to test the significance of the hypothesis that the population of $k_0$ has a mean greater than all of ...
6
votes
4answers
282 views

Why does the variance of the Random walk increase?

The random walk that is defined as $Y_{t} = Y_{t-1} + e_t$, where $e_t$ is white noise. Denotes that the current position is the sum of the previous position + an unpredicted term. You can prove that ...
0
votes
1answer
22 views

CausalImpact - Is pre-processing of covariates required?

I'm using the CausalImpact to evaluate the effect of a programme. My covariates are seasonal and I wonder whether I need to deseasonalise/ detrend the regressors before using the R package? Hal ...
1
vote
1answer
4k views

Warning messages from mixed model (glmer)

I ran a mixed model using lme4::glmer for a logistic regression and consistently got these warning messages. I noticed there are still regular results even so, but are they accurate estimates? ...
0
votes
1answer
12 views

Double expected value, which comes first?

In the following equation, the outer expectation is over the distribution $X_i|T_i = 1$ $\tau|_{T = 1} = E(E(Y_i|X_i, T_i = 1) - E(Y_i|X_i, T_i = 0)|T_i =1)$ Are we taking the expected value of ...
2
votes
1answer
66 views

How can I recreate a Weibull distribution given mean and standard deviation and the shape and scale parameters are unknown?

Figure 2 is a Weibull distribution of three different wind farms in Canada. These 3 probability distributions were combined in a study to obtain a common wind speed model. I will be using this common ...
0
votes
0answers
14 views

Regression model for Cumulative data in R

I am having a daily data for 3-4 months and another variable which is the cumulative sum. It starts with some value on the first day and it keeps on adding and at the end of 3 months, it would be sum ...
0
votes
0answers
5 views

Using spike-slab to fit log-link GLM Gamma

I am attempting to model the causal impact (using CausalImpact package in R) of a know discrete event on the change in medical expenditures. I have 12 pre and 6 post period observations and upwards of ...
1
vote
1answer
26 views

Generating 2D different shapes with same mean and covariance matrix

Im trying to reproduce the synthetic data used in this article. The authors claim the data was generated from densities with the same mean and covariance matrix. Is there any principled way I to ...
5
votes
4answers
200 views

Inverse function of variance

For a given constant number $r$ (e.g. 4), is it possible to find a probability distribution for $X$, so that we have $\mathrm{Var}(X)=r$?
0
votes
0answers
5 views

Change in r squared due to clustering in multiple linear regression

Puny undergraduate stats student here. I am examining the effect of two regressors on a predictor. OLS on the raw data (approx 200k cases) yields next to no correlation in the following models: ...
1
vote
1answer
102 views

Time series with correlated observations: How to start analysis?

We have a time series dataset: Daily arrivals of asylum seekers. Goal is to model this variable. In particular we would like to attempt Arima modeling and/or fitting a distribution. Before we get to ...
0
votes
0answers
8 views

What are “parts” in Haussler's definition of R-convolution kernels?

I have been reading about R-convolution kernels: http://citeseerx.ist.psu.edu/viewdoc/download?rep=rep1&type=pdf&doi=10.1.1.110.638. These important types of kernels are generalization of ...
0
votes
1answer
110 views

Locally weighted regression VS kernel linear regression?

I am trying to make it clear the relationship of the listed three methods. According to my understanding kernel regression means : the weight vector W lies in the space spanned by training data. $$ ...
1
vote
1answer
86 views

Kernel methods in machine learning?

I am beginning to tackle geostatistics problems where I tried to apply kriging(gaussian processes) to interpolate demographical water drop. According to my understanding, kernel methods are something ...
0
votes
0answers
13 views

Is there are better way of solving this problem than using a Neural Network Regression Model?

I'm working on power plant time series data from a SO2 absorption process and my main objective is finding out which independent variables are critical for reducing SAG (% of SO2 concentration ...
1
vote
1answer
19 views

Seemingly unrelated bivariate probit for endogeneity: interpretation of Rho

I would like to estimate the effect of health insurance coverage on type of healthcare provider chosen--either public or private--at last illness using a nationally representative sample of people in ...
0
votes
0answers
9 views

Random Variable Decomposition Standard Error

I have a decomposed random variable $X$ into partitions $A_1,A_2,\dots A_m$. I know how to compute the expected value of X and the variance of X given the variance and the standard errors of $X$ ...
0
votes
0answers
15 views

Poisson vs. Gaussian in Geomagnetic Data

I've been studying geomagnetic signals using a threshold approach to detect pulse events in the data. The question here is what is the significance of the crossover of stddev and mean as the ...
5
votes
0answers
101 views
0
votes
0answers
17 views

How to deal with an aliased predicator in a generalized linear model?

I am running a GLM model with a binomial distribution and a logit link function. The dependant variable is the number of changes in agricultural practices in a given households during the past 10 ...
2
votes
1answer
457 views

Log-likelihood ratio in document summarization

I initially asked this on stack overflow and was referred to this site, so here goes: I am implementing some unsupervised methods of content-selection/extraction based document summarization and I'm ...
2
votes
1answer
45 views
+50

Difficulties obtaining valid predictions when using interactions

I examine long term trends (2003 to 2014) for a continuous dependent variable. I want to predict the mean each year in relation to income category. Income is arranged in quintiles, from 1 (poorest) to ...
0
votes
0answers
6 views

repeated measures threeway linear mixed model

I'm new to using R, only started in the last 2 weeks so I apologize if I'm missing something very obvious. I am analyzing data from a repeated measures experiment with three factors, diversity (2 ...
0
votes
0answers
9 views

Noisy data in classifiers

I have a data set which has three classes(Labelled 1,2 and 3).However ,there is also noise in it. Should I classify with noise,so I will assign one more label to it (label 4) OR should I remove it ...
2
votes
1answer
30 views

Gaussian Process Regression for piecewise linear response functions

I am performing Gaussian Process Regression (without noise) for response functions which are piecewise linear. My question: Does there exist a covariance function, such that sample paths from a ...
0
votes
0answers
7 views

Back end processing of Pattern Matrix during EFA.

I want to know that how pattern matrix (using maximum likelihood and varimax rotation) is calculated. What is the underlying formula or pattern that displays such results? Please guide.
0
votes
0answers
9 views

SPSS - Identifying cases with identical case ID, AND similarity on another variable [on hold]

Say I've got data with 2 variables: ID, and code. ID | CODE 1 | 54 1 | 55 2 | 55 2 | 55 3 | 54 3 | 55 I want to make a new variable that identifies when cases ...
0
votes
0answers
29 views

How to test whether a random walk have a upward or downward trend

I have applied Kalman Filtering Method to get the estimates for time-varying coefficients (using DLM package in R). The model is like $S_{t} = \alpha_t * A_t + \beta_t * W_t + \gamma_t * A_t * W_t + ...
1
vote
1answer
790 views

Non-parametric correlation for continuous and dichotomous variables

I have two variables I want to test with correlation, one is continuous and the other dichotomous. My data are non-normally distributed, plus the variance is heterogeneous, so I have to apply a ...
0
votes
0answers
18 views

Fitting models to count data

Say we are fitting a model to count data (if needed we can assume it follows zero inflated Poisson model). To be specific, for each sample we have multivariate factors $X$, the (unknown) mean number ...

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