Skip to main content

Questions tagged [model]

A formalization of relationships between stochastically (randomly) related variables in the form of mathematical equations. DO NOT USE THIS TAG BY ITSELF: always include a more specific one.

Filter by
Sorted by
Tagged with
65 votes
7 answers
4k views

How much to pay? A practical problem

This is not a home work question but real problem faced by our company. Very recently (2 days ago) we ordered for manufacturing of 10000 product labels to a dealer. Dealer is independent person. He ...
Neeraj's user avatar
  • 2,310
49 votes
2 answers
77k views

Mixed Effects Model with Nesting

I have data collected from an experiment organized as follows: Two sites, each with 30 trees. 15 are treated, 15 are control at each site. From each tree, we sample three pieces of the stem, and ...
Erik's user avatar
  • 545
45 votes
4 answers
61k views

Should covariates that are not statistically significant be 'kept in' when creating a model?

I have several covariates in my calculation for a model, and not all of them are statistically significant. Should I remove those that are not? This question discusses the phenomenon, but does not ...
A.M.'s user avatar
  • 689
44 votes
2 answers
6k views

Why should we use t errors instead of normal errors?

In this blog post by Andrew Gelman, there is the following passage: The Bayesian models of 50 years ago seem hopelessly simple (except, of course, for simple problems), and I expect the Bayesian ...
Potato's user avatar
  • 1,105
40 votes
7 answers
6k views

Should parsimony really still be the gold standard?

Just a thought: Parsimonious models have always been the default go-to in model selection, but to what degree is this approach outdated? I'm curious about how much our tendency toward parsimony is a ...
theforestecologist's user avatar
35 votes
5 answers
10k views

Is an overfitted model necessarily useless?

Assume that a model has 100% accuracy on the training data, but 70% accuracy on the test data. Is the following argument true about this model? It is obvious that this is an overfitted model. The ...
Hossein's user avatar
  • 3,494
32 votes
5 answers
5k views

How can you account for COVID-19 in your models?

How are you dealing with the coronavirus "event" in your machine learning models? Let's say you used to predict the number of sales each month. The virus affected your results last year and ...
dsbr__0's user avatar
  • 827
31 votes
6 answers
9k views

In layman's terms, what is the difference between a model and a distribution?

The answers (definitions) defined on Wikipedia are arguably a bit cryptic to those unfamiliar with higher mathematics/statistics. In mathematical terms, a statistical model is usually thought of as ...
AlanSTACK's user avatar
  • 640
29 votes
4 answers
4k views

Is there any theory or field of study that concerns itself with modeling causation rather than correlation?

My understanding is that probability (at least from a frequentist viewpoint) is a mathematical tool for modeling correlations. So, for example, we can say that two events $X$ and $Y$ are defined to be ...
Maximal Ideal's user avatar
28 votes
1 answer
37k views

what happens when a model is having more parameters than training samples

In a simple neural network, say, for example, the number of parameters is kept small compared to number of samples available for training and this perhaps forces the model to learn the patterns in the ...
Upendra01's user avatar
  • 1,956
27 votes
8 answers
97k views

When forcing intercept of 0 in linear regression is acceptable/advisable [duplicate]

I have a regression model to estimate the completion time of a process, based on various factors. I have 200 trials of these processes, where the 9 factors being measured vary widely. When I perform a ...
Zack Newsham's user avatar
24 votes
3 answers
45k views

What is a null model in regression and how does it relate to the null hypothesis?

What is the null model in regression and what's the relationship between the null model and the null hypothesis? From my understanding, does it mean Using "an average of the response variable&...
Haitao Du's user avatar
  • 37.1k
22 votes
2 answers
4k views

What would be an example of a really simple model with an intractable likelihood?

Approximate Bayesian computation is a really cool technique for fitting basically any stochastic model, intended for models where the likelihood is intractable (say, you can sample from the model if ...
Rasmus Bååth's user avatar
21 votes
5 answers
4k views

Definition and delimitation of regression model

An embarrassingly simple question -- but it seems it has not been asked on Cross Validated before: What is the definition of a regression model? Also a support question, What is not a regression ...
Richard Hardy's user avatar
19 votes
3 answers
2k views

Widespread overfitting in health domain research?

I was reading about flaws with model selection techniques such as elimination based on significance and backwards selection via AIC (or similar) in the context of regression leading to inflated ...
JED HK's user avatar
  • 409
19 votes
3 answers
18k views

What are real life examples of "non-parametric statistical models"?

I am reading the Wikipedia article on statistical models here, and I am somewhat perplexed as to the meaning of "non-parametric statistical models", specifically: A statistical model is ...
Creatron's user avatar
  • 1,665
18 votes
1 answer
9k views

The difference between with or without intercept model in logistic regression

I like to understand the difference between with or without intercept model in logistic regression Is there any difference between them except that with the intercept the coefficients regard the log(...
user148087's user avatar
18 votes
2 answers
745 views

Statistical Inference Under Misspecification

The classical treatment of statistical inference relies on the assumption that that a correctly specified statistical is used exists. That is, the distribution $\mathbb{P}^*(Y)$ that generated the ...
Julian Karch's user avatar
  • 1,980
17 votes
3 answers
2k views

What is the difference between a "statistical experiment" and a "statistical model"?

I am following A.W. van der Vaart, asymptotic statistics (1998). He talks of statistical experiments, claiming that they are different from a statistical model, but he defines neither. My question: ...
Mikkel Rev's user avatar
17 votes
5 answers
14k views

Is R-squared truly an invalid metric for non-linear models?

I have read that R-squared is invalid for non-linear models, because the relationship that SSR + SSE = SSTotal no longer holds. Can somebody explain why this is true? SSR and SSE are just the ...
Greg's user avatar
  • 323
16 votes
3 answers
31k views

Should I remove non-significant variables from my regression model

I have run a multiple linear regression using stepwise regression to select the best model, however the best model returned has a non-significant variable. When I remove this the AIC value goes up ...
Poppy's user avatar
  • 161
16 votes
1 answer
1k views

Is there any "standard" for statistical model notation?

In, for example, the BUGS manual or the upcoming book by Lee and Wagenmakers (pdf) and in many other places a type of notation is used that to me seems very flexible in that it can be used to ...
Rasmus Bååth's user avatar
15 votes
1 answer
16k views

Interpreting the regression output from a mixed model when interactions between categorical variables are included

I have a question about my use of a mixed model/lmer. The basic model is this: lmer(DV ~ group * condition + (1|pptid), data= df) Group and condition are both ...
vizzero's user avatar
  • 275
15 votes
3 answers
2k views

How can I fit a spline to data that contains values and 1st/2nd derivatives?

I have a dataset that contains, let's say, some measurements for position, speed and acceleration. All come from the same "run". I could construct a linear system and fit a polynomial to all of those ...
dani's user avatar
  • 203
15 votes
1 answer
16k 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 ...
ciaran_r's user avatar
  • 153
15 votes
3 answers
2k views

What are the options in proportional hazard regression model when Schoenfeld residuals are not good?

I am doing a Cox proportional hazards regression in R using coxph, which includes many variables. The Martingale residuals look great, and the Schoenfeld residuals ...
jeffalstott's user avatar
14 votes
8 answers
35k views

Flexible and inflexible models in machine learning

I came across a simple question on comparing flexible models (i.e. splines) vs. inflexible models (e.g. linear regression) under different scenarios. The question is: In general, do we expect the ...
alittleboy's user avatar
  • 1,013
14 votes
1 answer
3k views

Is MLE estimation asymptotically normal & efficient even if the model is not true?

Premise: this may be a stupid question. I only know the statements about MLE asymptotic properties, but I never studied the proofs. If I did, maybe I woulnd't be asking these questions, or I maybe I ...
DeltaIV's user avatar
  • 18.1k
13 votes
4 answers
8k views

Can Tree-based regression perform worse than plain linear regression?

Hi I'm studying regression techniques. My data has 15 features and 60 million examples (regression task). When I tried many known regression techniques (gradient boosted tree, Decision tree ...
amityaffliction's user avatar
13 votes
2 answers
627 views

Given two linear regression models, which model would perform better?

I have taken up a machine learning course at my college. In one of the quizes, this question was asked. Model 1 : $$ y = \theta x + \epsilon $$ Model 2 : $$ y = \theta x + \theta^2 x + \epsilon $$ ...
kush's user avatar
  • 133
13 votes
2 answers
28k views

How to know if model is overfitting or underfitting?

I understand that using cross validation we can validate our model, but it is also possible that maybe our model is underfitting; hence, providing wrong results. One possibility that I can think of is ...
DKP's user avatar
  • 241
13 votes
1 answer
1k views

Geometric interpretation of generalized linear model

For linear model $y=x\beta+e$, we can have a nice geometric interpretation of estimated model via OLS: $\hat{y}=x\hat{\beta}+\hat{e}$. $\hat{y}$ is the projection of y onto the space spanned by x and ...
Vincent's user avatar
  • 131
12 votes
4 answers
2k views

Clues that a problem is well suited for linear regression

I am learning linear regression using Introduction to Linear Regression Analysis by Montgomery, Peck, and Vining. I'd like to choose a data analysis project. I have the naive thought that linear ...
cwackers's user avatar
  • 215
12 votes
1 answer
1k views

How did Cross Validation become the "Golden Standard" of Measuring the Performance of Statistical Models?

I have the following question: How did Cross Validation become the "Golden Standard" of Measuring the Performance of Statistical Models? I understand the "logical appeal" of Cross ...
stats_noob's user avatar
12 votes
1 answer
5k views

Generalised additive model: What is ref.df in R's output?

Hi I am struggling to understand Ref.df in the output screen in R: ...
Tania Mendo's user avatar
11 votes
7 answers
812 views

Avoiding social discrimination in model building

I have questions inspired from the Amazon recent recruitment scandal, where they were accused of discrimination against women in their recruitment process. More info here: Amazon.com Inc's machine-...
Lucas Morin's user avatar
  • 1,655
11 votes
1 answer
11k views

Model selection for GAM in R

Apologies in advance I new to this forum and to GAM models. I am trying to model complex ecological data. I have programmed a lot of GAM models using the mgcv ...
Kilian Murphy's user avatar
11 votes
3 answers
3k views

Bayesian model selection and credible interval

I have a dataset with three variables, where all variables are quantitatives. Let call it $y$, $x_1$ and $x_2$. I'm fitting a regression model in a Bayesian perspective via MCMC with ...
user avatar
11 votes
3 answers
20k views

What is no ' information rate ' algorithm?

I plan to implement ' no information rate ' as part of summary statistics. This statistic is implemented in r (Optimise SVM to avoid false-negative in binary classification) but not in Python (at ...
blue-sky's user avatar
  • 637
11 votes
1 answer
3k views

How can I tell if a statistical model is "identified"?

My econometrics professor used the term "identified" in class. We are considering data generating processes of the form $$Y = \beta_0 + \beta_1 X + U$$ where $X$ is a random variable and $U$ is a ...
Stan Shunpike's user avatar
11 votes
2 answers
178 views

Estimate The Rate At Which Standard Deviation Scales With An Independent Variable

I have an experiment in which I am taking measurements of a normally distributed variable $Y$, $$Y \sim N(\mu,\sigma)$$ However, previous experiments have provided some evidence that the standard ...
Adam Bosen's user avatar
10 votes
4 answers
4k views

Why is KNN not "model-based"?

ESL chapter 2.4 seems to classify linear regression as "model-based", because it assumes $f(x) \approx x\cdot\beta$, whereas no similar approximation is stated for k-nearest neighbors. But aren't both ...
alecbz's user avatar
  • 545
10 votes
3 answers
2k views

Where did the term "learn a model" come from

Often I have heard the data miners here use this term. As a statistician who has worked on classification problems I am familiar with the term "train a classifier" and I assume "learn a model" means ...
Michael R. Chernick's user avatar
10 votes
2 answers
16k views

Fitting exponential decay with negative y values

I am trying to fit an exponential decay function to y-values that become negative at high x-values, but am unable to configure my nls function correctly. Aim I ...
Mikko's user avatar
  • 1,332
10 votes
1 answer
15k views

How to fit piecewise constant (or step-function) model and compare to logistic model in R

I have x, y data where x is position (along a transect) and y is a continuous variable (e.g. temperature). ...
Julie L's user avatar
  • 313
10 votes
2 answers
716 views

Best practices when treating range data as continuous

I am looking at whether abundance is related to size. Size is (of course) continuous, however, abundance is recorded on a scale such that ...
Trees4theForest's user avatar
10 votes
2 answers
1k views

What would be a good model to fit to cumulative reputation on Stack Exchange?

I'm trying to model the distribution of my cumulated reputation on one Stack Exchange site over time (that is, each data point is the sum of my reputation whenever that reputation changes, mostly ...
user avatar
10 votes
2 answers
1k views

Why are Ratios "Dangerous" in Statistical Modelling? [closed]

Why are Ratios "Dangerous" in Statistical Modelling? A friend was telling me today that it is unwise to use the ratio of two variables as a variable in a regression model, and that is better ...
stats_noob's user avatar
10 votes
1 answer
7k views

How much is too much overfitting?

Conceptually, where do you draw the line between an overfit model and adequately fit model? It's clear that if your model is doing a couple percent better on your training set than your test set, you ...
foboi1122's user avatar
  • 233
10 votes
4 answers
563 views

How to model the probability of a truth claim given an arrangement of eyewitness accounts supporting specific instances of that claim?

Note: the title is adapted from my Philosophy.SE question Epistemic value of multiple eyewitness accounts: single event vs. multiple events given a fixed number of eyewitnesses?. See meta discussion ...
user avatar

1
2 3 4 5
29