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Questions tagged [prediction]

Prediction of unknown random quantities, using a statistical model.

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What is the marginal variance of the mean of $y$ if I only know the variance of the conditional mean of $y$?

Suppose an estimate for the conditional mean of $y$ given $x$ is $\hat{E}(y|x)$. Suppose the variance (or the variance estimate) of $\hat{E}(y|x)$ is known to be $V(\hat{E}(y|x))$ for all $x$. The ...
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Associating a quality/confidence score with a regression model

Let's say I have a set of $N$ data points $x_n \in \mathbb{R}^M, \; n=1,2,\ldots,N$. Say I have trained a model $f:\mathbb{R}^M \mapsto \mathbb{R}$ on a separate training set (using linear regression, ...
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What do we use variance of the error term for in regression analysis?

So I get that, for simple linear regression where Y = B_0 + B_1(x) + E, Var(Y|x) = Var(E). Variance of the mean response involves it, as does variance of future responses, but is this ever actually ...
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How to prove High Sampling Variance in over-fitted functions

I've been reading recently about over-fitting and it is frequently related to High Sampling Variance and Low Bias characteristics. However, what is the metric used to state the High Sampling ...
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My ASReml GLMM is predicting “NA” values for one of my variables, any suggestions on how to fix this? [on hold]

My question is: What is the relationship between malaria and schistosomiasis? Therefor, I have plotted this GLMM; ...
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20 views

What is the difference between model prediction and model estimation? [duplicate]

Could you please explain for me what's the difference between model prediction and model estimation ? What is the difference between a prediction interval and an estimation interval?
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43 views

Inference and prediction with skewed response

Here below 2 questions. A typical linear model assumption says that the error terms are normal, which implies that the conditional distributions of Y given X=x are normal. The unconditional (marginal)...
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1answer
50 views

How to predict routes using clustering data

I've been working on a ship route prediction algorithm such that given the past and current trajectory of a ship I am able to estimate the future one. The trajectories are represented as a sequence of ...
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Unsatisfactory prediction error - is it possible to improve accurancy? [duplicate]

I'm green in ML field and I try to classify user reports to valid/invalid. My dataset contains of Valid - 7355 samples Invalid - 6285 samples So, I devide data into train and test ...
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Looking for advice: Short-term forecasting using actual forecasts and real time data

First of all apologies, I have very little experience in statistics and my biggest problem is using the correct terminology. I'm here mainly looking for guidance and direction. Background: I have a ...
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14 views

Making predictions in a Non-Linear State-Space Model

Our SSM is the following: $$ x_{t+1} = F(x_{t}) + w_{t+1}\\ y_t = G(x_t) + v_t $$ My main doubt is if we know $x_T$ how does one predict $x_{T+1}$? If I have an estimate of $F$, $\hat F$, could ...
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48 views

bayesian predictions in multilevel model for panel data

I want to make predictions for a bayesian multilevel which basically looks like: $y_{it} = \alpha_{i} + x_{it}\beta$. I was told that I could make predictions by using (in the case that $y_{it}$ is ...
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Why would standard errors around each individual point of an ARIMA model converge to a single value?

I am using an AR(1) model to determine if a new data point is beyond some expected range. Typically, you could use a prediction interval, which would just be the predicted value, +/- some multiple (e....
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12 views

Geisser's definition of nonstochastic prediction

Does anyone have Geisser, 1993, "Predictive Inference: An Introduction." Chap- man and Hall, London. MR1252174? I am interested in the definition on page 31 for "nonstochastic prediction," but unable ...
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Quantifying uncertainty of predictions for new data in the regression tree

I used Regression Learner to train my data. I held out 25% of the input for validation and ran different models for training. Based on the results using RMSE and R-squared, I decided to go for the ...
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1answer
29 views

Modelling approach - tennis match predictions

I am working with a dataset about a fictitious type of sport which is fairly similar to tennis: One has to win 5 points to win a game, 4 games to win a set and 3 sets to win the match. However, there ...
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Dichotomizing a Continuous Variable vs. using z-score (z < - 2.5 as threshold): equally bad?

May we regard Dichotomizing a Continuous Variable vs. using its z-score (ex.: z < - 2.5 as a threshold) equally bad?
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1answer
28 views

Which statistical test to use for this dataset?

I have a dataset that I need to analyse in order to determine if the measurements taken can be used to predict tool type. The independent variable (tool type) has 6 groups. From each of these tools 8 ...
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1answer
27 views

Conditional Logistic Regression in R

As my first question addressing this matter was incomplete and unclear, I made another attempt with an improved outline. I am currently working on a project in which I have a data-set of the following ...
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27 views

Prediction intervals of a log return time series when converting back to levels

I am having a problem when calculating prediction intervals of an ARIMA model of a log return transformed time series. Assume I have the following point estimates for $h \in \{1, 2, 3\}$ where $y_{t+...
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1answer
48 views

Variance of sum of dependent random variables

Can you guys help me prove the following: $$ \operatorname{Var}\left[\frac{1}{m}\sum_{i=1}^my_i\right]=\frac{1}{m}(1-\rho)\sigma^2+\rho\sigma^2 $$ where the sampled predictions ($y_is$) have ...
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2answers
43 views

How to use last predicted value as feature? NLP NER mission

I'm performing NER (Named entity recognition) For example: Seq: When Donald Trump announced... Tags: O B-Person L-Person O When I'm predicting ...
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1answer
40 views

Predictability of a time series

Say we are given a time series $(x_t)_{t \in P}$ where $P$ is the index set of past observations (train set). Imagine that we have built a model for our data and now want to assess predictability of ...
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29 views

Measure of accuracy for a Bayesian model

I am reading Statistical Rethinking (Section 6.2.1.2). The topic of this section is measuring accuracy for a Bayesian model, i.e. accuracy of the model of predicting correctly an outcome. The ...
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High dimensional regression overfitting

Consider the linear regression model \begin{equation} \boldsymbol{y} = \boldsymbol{X}\boldsymbol{\beta} + \boldsymbol{\epsilon} \end{equation} where we assume $\boldsymbol{X}$ is $n$-by-$p$, with $p &...
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Are there two motivations for Bayesian information criteria?

Are there two motivations for all these Bayesian information criteria? I am only aware of the motivation of "expected out-of-sample prediction score." Let the in-sample data be $y$ and the parameter ...
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1answer
47 views

How can I create a meaningful weighting for RMSE?

Background I should start off by saying I am not a mathematician and please excuse simple/stupid mistakes! The goal of my exercise is to find the “best-fitting” model for the purpose of prediction. ...
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1answer
132 views

Strategies for predicting 100 binary choices given the previous 100

Background As an experimental psychologist, I've long had an interest in binary decision-making tasks. Typically, in such a task, I manipulate a few properties of some hypothetical or real decision, ...
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15 views

2SLS - With the same X's and Z's but 3 different Y's, Over-identification and Hausman tests give different results

Dear Stack Exchange users, Let's say that my independent variable of interest is expected to be endogenous and that I want to investigate its effect on 3 separate outcomes. I run separate 2SLS's (one ...
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13 views

What information can be extracted from plotting two variables of a multiple logistic regression against the prediction?

I have a multiple logistic regression model that has the form of: disease ~ treatment + x2 + x3 + x4 + x5 Where disease can take values 1 and 0 (diseased or not ...
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1answer
21 views

What is the formal name of the following problem: predicting next output based on previous values, NOT a sequence

I have a system producing an output periodically, I would like to build a model to predict the next entry. There is no sequence relation between the output values, order doesn't matter. The only ...
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1answer
19 views

How to calculate price prediction model accuracy from metrics such as MAE and MSE

I am new to both statistics and machine learning in general. I've tried to construct a price prediction model using the RNN-LSTM architecture. For this problem I have a dataset of one-minute closing ...
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With omitted variables is OLS estimator still the best linear predictor?

Suppose the true model is $$y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \epsilon$$ where $x_1$ and $x_2$ are correlated and $\epsilon$ is white noise. I omit variable $x_2$ and apply OLS to estimate $...
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How to use the multilayer perceptron to predict the price of an action with and without news?

I have a multilayer perceptron that predicts the value of a stock market given news articles. I would like that at a given moment I do not use the news and predict the value until I have other values. ...
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Machine Failure Prediction through parameter degradation

The problem that I've in hand is there is a temperature value(time series data) which keeps on increasing linearly during the manufacturing process and after it reaches a threshold, the process should ...
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Calculating probability of voting results from small sample aize

2 million people voted in a poll for their favourite song. 35,000 votes have been counted so far, of which: 575 votes are for Song A 466 votes are for Song B 393 votes are for Song C (And the ...
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Repeated measures to predict binary outcome

I would like to predict outcome of an event based on repeated measures. The problem is the following one: I have 100 patients with measures of a certain feature at different times, but all the ...
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1answer
218 views

Which estimation technique minimizes the MAPE?

Suppose we have two estimation techniques: Linear Least Squares, which aims to minimize squared residuals Least Absolute Deviation, which aims to minimize absolute residuals We have a model, which ...
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2answers
56 views

Forecasting the number of visitors in each hotel in a city

I am looking for some suggestion on what a good approach would be for the following forecasting problem. Problem statement: There are 100 hotels in a city and I have the monthly data on the total ...
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1answer
45 views

cross-validation analysis not diagnostic

I'm using k-fold cross-validation analysis for model selection, however, it does not appear to favor any particular model. There are several variants of the models and two of them are nested within (...
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1answer
22 views

Predict Based on Prediction?

I am working on a binary classification task with a pretty straightforward input set of numeric features. One of these features is particularly good, but it cannot be used in real life because it's a ...
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1answer
50 views

Why does GBM package make different predictions for the same data point (after factor issue is fixed)?

I tried to use a GBM model to make predictions for the same data point, but it gave me very different answers. Please see the example below. When using the entire dataset for predicting the first data ...
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1answer
24 views

Correct Type of Statistical/Machine Learning Analysis For Inflow

I want to predict the number of people joining (inflow e.g. 4000, 5000, 6000 etc) online subscription. The dependent variable is ‘inflow in the first 4 weeks for a certain content title’ as this is ...
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2answers
66 views

Reality check: Given $k$ and an estimate of $n$, solve $B(n,p)$ for $n:p\approx1$?

Back Pedaling Honestly, I'm not even sure if this is the right question but I haven't been able to come up with anything that makes more sense so I'd appreciate some help. This is a real problem, not ...
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1answer
18 views

Holt Winters estimation of parameters in R

Which type of error is R trying to minimise to estimate alpha, beta, gamma when you make a Holt Winters model? I looked it up in help and here it is stated as "the squared one-step prediction error" ...
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15 views

How to compare predictions made by two different models?

Please help me understand this: There's something called the MAGGIC Risk Calculator for Heart Failure for predicting adverse outcomes in patients with heart failure....
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17 views

Modelling two correlated Time Series

as data that I have : Two boats latitude and longitude (red and yellow graphs) timestamp I am trying to create a model that would predict when a boat enters or exits a port ( when the variation of ...
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1answer
22 views

How can I predict movie rating based on actors/directors in it?

Okay so my goal is to create a program to predict average rating of a movie, based on release date, director and actors playing in it. Some movies have one/two directors, some movies have one/two/...
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36 views

Using ecmpredict in R to forecast from an ECM

I have fit an ECM model to my data using the ecm function, which is part of the ecm package. I would now like to use ecmpredict to predict/forecast future values of my target variable. The function ...
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14 views

Updating the odds / probability once user plays a bet

I am working on an assignment in which I have created a game in which user will bet on outcome of a game. Team A will win or not. if win = Invest 7 get 10 if not win = Invest 5 get 10 I have an ...