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0 votes
0 answers
31 views

Missing values in data set before DBSCAN

My goal is to identify bots and fraudulent users for an application. Ideally, this would be a regression problem where users are rated on a continuous scale. I have 4 tables that cover different ...
0 votes
0 answers
17 views

Diagnostic checks before and after adjusting for standard errors in R

Currently, I'm fitting a regression model for my dataset. As there are clusters, I refitted the model using the coeftest() function from the ...
1 vote
1 answer
48 views

Which kind of analysis could be made to associate a set of genes to clinical values?

I have a set of 5 genes that can be mutated or not, so therefore are intended as dichotomous yes/no vars. I want to identify the effect of the mutation of this genes on a continuous response var. The ...
4 votes
3 answers
351 views

Outlier detection methods aware of target variable

I am trying to predict ambulance demand for the next hour, for a city area in the USA, based on previous demand, weather, large people gatherings, and similar spatio-temporal factors. I have noticed ...
2 votes
1 answer
91 views

Is certain data better for Fixed effects compared to Mixed effects?

I think it is an obvious question with an obvious answer, but I want to confirm. In hierarchical regression models, if some clusters have very few observations, the coefficients for those clusters ...
0 votes
2 answers
4k views

Can we use cluster analysis in multiple regression

I am quite new to Data Analytics. I was just wondering whether we can use cluster analysis in Multiple Regression. Let me give you a scenario so that it becomes easier to visualize. I have a dataset ...
0 votes
0 answers
54 views

Weighted regression with no variance within cluster

Given a clustered data set with no variation within a cluster, shouldn't a regression weighted with the inverse cluster size give the same results as a regression with only one observation per cluster?...
1 vote
0 answers
58 views

Clustering or factor analysis for dimensionality reduction in multivariate linear regression

I have dataset describing aggregated purchases from multiple brands. It contains variables: Brand (ordinal) Promotion (ordinal) Sales (numeric) I need to use linear regression to describe the effect ...
1 vote
0 answers
36 views

Regression with unlabeled data from several clusters

I want to characterize the relation of a few input parameters to a single output parameter. The problem I have is that my data is collected from several groups. The groups are defined both by the ...
2 votes
1 answer
445 views

Latent Class Analysis - Interpretation and integration with survival analysis?

I am approaching to Latent Class Analysis to identify "classes" of patients based on some variables. Question 1: diagnostics of the results. I already gone through the discussion on whether ...
1 vote
0 answers
19 views

References about clustered linear regression

I have to write a project about clustered linear regression. The only resource I have been able to find is https://doi.org/10.1016/S0950-7051(01)00154-X Any other articles/books that you can point me ...
3 votes
2 answers
764 views

Is 'High School', 'Graduate', 'Unknown' ordinal or nominal data?

My goal is to Feature Engineering the column Education_Level. This is an obvious ordinal data. However, I am having difficulty to put Education_Level to choose <...
0 votes
0 answers
81 views

overlapping clusters in R

I have a bunch of observations that are correlated with each other in two different ways, so I'd like to perform logistic regressions but try to account for the correlations among observations to get ...
0 votes
0 answers
15 views

Robust parameter optimization optimizing profit using clustering, classification or regression?

Imagine an algorithm with a parameter applied to different applications. For each application and parameter I can estimate the performance of the algorithm using simulations and historic data. ...
1 vote
2 answers
296 views

How to sort multiple time-series based on "shape"?

Consider the following toy data: All prices have been normalised using $${Price_{Norm} = \frac{Price_{current} - Price_{Min}}{Price_{Max} - Price_{Min}}}$$ Item : Prices Apples : 0.0, 0.4, 0.8, ...
18 votes
5 answers
3k views

Fitting an Orthogonal Grid to Noisy Points

I have a list of coordinates that are meant to form an orthogonal grid that could be rotated. The grid is not necessarily uniform. The rotation is not typically greater than 10°. The coordinates are ...
2 votes
1 answer
116 views

Different Meanings of "Clusters" in Statistics

Typically, I have always come across the term "cluster" within statistics as reference to "clustering" - that is, for example the "K Means Clustering" algorithm. Recently,...
0 votes
0 answers
557 views

When we use k-means clustering with Light GBM, comparing with Random Forest

I am developping the prediction model with many parameters. As I was not satisfied by the performance of Random Forest Regression, I tried to use k-means clustering to regroup the similar variable and ...
0 votes
0 answers
276 views

cluster by multiple linear regressions

If I have an X and Y feature, can I cluster by what linear regression two populations fall on? Consider: In the pink is one regression. In the yellow is another. I know that these two lobes are ...
1 vote
0 answers
50 views

Regression With Multiple Groups in a Dataset

I am trying to predict grocery store sales from the number of cars in the parking lot. I have a dataset of around 400 stores, over two years, with a count of the number of cars in the lot at the same ...
1 vote
0 answers
122 views

How to use DecisionTreeClassifier on a problem involving ranks as features? [closed]

Let's say we have a dataset with these columns: A | B | C | D | E | F | G I want to predict [E,F,G] based on [A,B] with following rule: top 5 entries order by (SUM(D)/SUM(C)) desc Pseudo-SQL query ...
1 vote
1 answer
77 views

Guidance with piecewise linear data set

I have data that looks like: As you can see my linear modeling doesn't really work since the y values increase and then stay constant. I want to separate my data for each group into 2 and then ...
1 vote
0 answers
37 views

How to group cities with similarity in order to perform a regression?

My objective is to understand if the average number of students in the classrooms can lead to better grades in a specific exam to all high school students of the cities in a country. My country has ...
2 votes
1 answer
419 views

linear regression inappropriate when there are multiple underlying groups

In this scenario, one group has a clear linear relationship between x and y. Another group doesn't. However, there is no way to differentiate them in the data. In this case, performing a simple linear ...
2 votes
0 answers
275 views

Inference with Mixture of Linear Regression

I have used an EM algorithm to fit a finite mixture of linear regression to my data, and cluster them into $k$ clusters. Now that I have my clusters with the estimated parameters $\beta_k$ and $\...
0 votes
0 answers
66 views

Customer segmentation variable selection

I am trying to do customer segmentation based on their past purchase pattern on different segments like milk products, frozen products, alcohol, grocery and so on I also have each customer's ...
1 vote
2 answers
674 views

Is it a good idea to cluster predictor variables to try an improve classification performance with logistic regression?

I have trained a logistic regression model on on a selection of 10 socio-demographic predictor variables, all of which are categorical, in order to predict customer behavior on an outcome measure. Out ...
1 vote
0 answers
39 views

selecting data based on colinearity

I have two variables that are supposed to correlate with each other across the whole dataset, but as you can see in the scatter plot below, it appears that I have a mix of two sub-samples. One in ...
0 votes
0 answers
50 views

Cluster regression

I have a dataset and divided the sample into 6 groups based on 4 binary criteria (e.g. "1" for has a Chief Digital Officer and "0" otherwise). Now I want to conduct a regression of ...
1 vote
2 answers
4k views

Logistic regression vs clustering analysis

I am having some trouble understanding the difference between clustering and logistic regression. Can you give me some examples of when and why it would be better to use clustering instead of logistic ...
3 votes
1 answer
1k views

Minimize the sum of squared perpendicular distances while computing PCA

Problem (PCA): Assume that p = 2 and the the predictors are centered. Show that the sum of squared perpendicular distances from ($x_{i1}, x_{i2})$, i = 1, 2, . . . , n to the line $a_{2}x_{1}−a_{1}x_{...
2 votes
2 answers
812 views

What are the advantages of segmentation?

Suppose you want to fit a regression model. You have a data set with multiple attributes. What would be the advantages of segmenting the data set and fitting a regression model to each of the segments?...
9 votes
1 answer
196 views

Question on Inference - Catching Cheating Students

In their paper "Catching cheating students", Levitt and Lin propose a simple reduced-form method to identify cheating of students in exams. The strategy works as follows: For each possible pair of ...
2 votes
1 answer
69 views

Linear regression on clustering result

I've got a data about mammal body weight responses to increasing air temperature. I want to know whether there are some mammals that respond to the increasing air temperatures differently. Hence, I ...
2 votes
1 answer
50 views

What stastical modeling techniques I can use to estimate the boundary of data points in an unsupervised way

Suppose I have a dataset (see the explanation of the background later) as shown in the plot below, where each dot is a sample. To human eyes, there is an obvious boundary which separates samples above ...
1 vote
0 answers
168 views

How to manually inflate standard errors to approximate clustered SEs

I'm reading a handout on clustering here It's not clear to me how to compute $\rho_x$ or $\rho_\epsilon$. What is meant by within-cluster correlation of the regression, or within-cluster error ...
1 vote
0 answers
36 views

Clustering coefficients of sum-of-sine regression models

I am trying to cluster different time series, based on coefficients yielded by sinusoidal regression. In my case, I have a number of time series of equal length and I fit a sinusoidal regression with ...
1 vote
0 answers
36 views

Selecting a representative graph [closed]

I have a bunch of graphs that if one looks at them can see that they share a clear trend. My question is how to pick a representative graph? Would it make sense to standardize all curves first and ...
1 vote
2 answers
851 views

Question about "curve fitting" using ML without known functional form

I am fairly new to machine learning, so I apologize if this is a bad and/or repeat question. It does seem like questions of this nature have been asked at least for linear relationships. Let's say ...
1 vote
0 answers
15 views

Applying clustering to predicted values

I am using clustering techniques such as hierarchical clustering trees to create an index fund modeled on the S&P500 with the correlation between the returns of individual stocks being used as the ...
1 vote
0 answers
131 views

What type of algorithm should I use to analyse questionnaires answers?

Let's say I have a questionnaire, part of it are multiple-choice answers. The person answering may answer (a), (b) or (c) for the first question, then (a), (b), (c), (d), (e) or (f) for the second ...
2 votes
1 answer
886 views

Why data shuffling has such a dramatic effect in K-Neighbours regression?

I am trying to use the K-Neighbouts for regression and I find to my surprise that not shuffling the training data has a huge effect on the quality of the prediction. With shuffling. 98% training data:...
4 votes
1 answer
450 views

What algorithm to use for fitting several different lines

I have a unique problem I'm not sure how to approach. I have some data. The data was generated by a function that's basically $k$ different lines ($k$ may or may not be given). Example: However, ...
1 vote
0 answers
52 views

Pooled panel regression with group-wise clustering by time

How can I compute t-statistics for the coefficients of the pooled panel regression model below such that I account for group-wise clustering by time? $$ Y_{it} = \alpha + \beta x_{it} + \epsilon_{it} ...
3 votes
1 answer
63 views

Boundary estimation using statistical techniques

Do you know a good methodology to estimate the boundary between two sets? Here are the specifics of the problem: I am studying a recursion defined as $$ 2(n+q) x_{n+2}= (r(n+q) +s)x_{n+1}+((2-r)(n+q)...
1 vote
1 answer
48 views

Distinighsing Between Groups on a Bimodal Varaible

I am working with the diamonds data set from the tidyverse package in R. library(tidyverse) View(diamonds) When I plot a histogram of the price variable with 300 ...
3 votes
1 answer
1k views

How can the clustered robust standard errors be smaller than the model based ones?

I ran a GEE model and I used it to check the difference between the empirical standard errors and the model-based one. For almost the variables, the empirical standard error was greater than the OLS ...
0 votes
1 answer
17 views

Identify subsets of points that have different correlation

Apologies for the vague question. I have a plot similar to the figure below, where I compare the distribution of two variables, i.e. the allele frequency in different datasets. As you can see most ...
0 votes
1 answer
61 views

How to predict the second most likely categorical feature?

Suppose I have a dataset $X$ that contains both numerical and categorical features. For concreteness let's assume that one of the categorical features is a sample's ...
0 votes
0 answers
183 views

Classifying data, and then performing linear regression on the classes

Is it valid to perform a classification on a data set, separate the data by class, and then perform a regression on each of the groups? The reason why I ask is that the histogram for my data looks ...