Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [recursive-model]

35 questions
9
votes
2 answers

Intuition for recursive least squares

The least squares formula, $\beta = (X'X)^{-1}X'Y$ can be recursively formulated as \begin{align} \beta_t &= \beta_{t-1} +\frac{1}{t}R_t^{-1}x_t'(y_t-x_t\beta_{t-1}),\\ R_t &= R_{t-1}+\frac{1}{t}(x_t'x_t-R_{t-1}), \end{align} where $\beta_{t}$…
Har
  • 1,494
  • 11
  • 15
7
votes
2 answers

Population model to model year to year dynamics

My task is to assess how various environmental variables affect annual population fluctuations. For this, I would use a model like: $$ \mbox{log} ( \mu_{i,j+1} ) = \mbox{log} ( \mu_{i,j} ) + R_{j} + \sum\limits_{k} \alpha_k x_{k,j} \\ N_{i,j} \sim…
Tomas
  • 5,735
  • 11
  • 52
  • 93
6
votes
1 answer

Can neural network can be used to predict pseudo-random numbers?

Given list of numbers which looks pseudo-random (like lotto numbers, stock prices, pseudo-random), is it is possible to train the network to attempt to predict the next numbers? Which network would be more suitable for this task? Feedforward,…
kenorb
  • 549
  • 2
  • 7
  • 19
5
votes
1 answer

Predicting the observations in a POMDP with a recurrent neural network

I use neural networks for online sequence prediction. The performance of LSTM in this case, however, is not nearly as good as I expected. Maybe someone can help me understand where the problem lies. The peculiarity of online learning is that there…
wehnsdaefflae
  • 536
  • 3
  • 14
5
votes
3 answers

Recursive neural network implementation in TensorFlow

Is there any available recursive neural network implementation in TensorFlow TensorFlow's tutorials do not present any recursive neural networks. Most TensorFlow code I've found is CNN, LSTM, GRU, vanilla recurrent neural networks or MLP. I am most…
Franck Dernoncourt
  • 42,093
  • 30
  • 155
  • 271
3
votes
1 answer

Recursive Feature Elimination in sklearn

I have been thinking about one thing after reading documentation from sklearn about Feature Selection for building prediction models (http://scikit-learn.org/stable/modules/feature_selection.html#rfe) More specifically 1.13.3 --> Recursive Feature…
Pablo Ruiz Ruiz
  • 186
  • 7
3
votes
1 answer

Proportion of cells with certain number of mutation in dividing population

I managed to reduce a problem in my research to the following: Suppose that we have a population of cells which starts with a single cell that has zero mutations, and at each time step each cell divides in two. After each division, one of the…
Alex
  • 295
  • 1
  • 7
2
votes
1 answer

RFE: Pre-define a specificity threshold

I would like to use recursive feature elimination (implemented via caret in R) to perform feature selection for about 40 test results with 2 possible outcomes. Consequently, RFE either models by Accuracy or by Kappa. Now, I would like to pre-define…
Felix
  • 33
  • 4
2
votes
1 answer

How to model a recursive probabilistic experiment?

I have a theoretical experiment as follows: We have two boxes A and B, and N unfair coins (each has different probability for showing Heads). At the beginning, box A contains all N coins and box B is empty. the experiment includes following ordered…
Angie
  • 143
  • 5
2
votes
0 answers

How to derive a recursive version of a regularised cost function

I am to derive a recursive version of the following cost function and examine for which choice of D can we have a estimator windup $V(\theta) = \frac{1}{2}\sum_{t=1}^n(y(t)-\phi(t)^T\theta)^2 + \theta^TD\theta$. I do not really see if my answer is…
J.Doe
  • 21
  • 1
2
votes
1 answer

MLE derivation of the Recursive Least Squares estimator

I think I'm able to derive the RLS estimate using simple properties of the likelihood/score function, assuming standard normal errors. If the model is $$Y_t = X_t\beta + W_t$$ then the likelihood function (at time $N$) is $$L_N(\beta_{N}) =…
measure_theory
  • 535
  • 2
  • 7
2
votes
0 answers

Recursive nonlinear models

I have the following recursive linear model $X = \varepsilon_1$ $Z = \beta_2 X + \varepsilon_2$ $Y = \beta_3 Z + \varepsilon_3$ By solving the model its easy to see that $E[Y|X, Z]$ is linear too. But what happens in the nonlinear case? Let's say I…
user2746008
  • 21
  • 1
2
votes
1 answer

Seemingly simple urn stochastic process

This seems like a simple model, but I'm getting a bit stuck on it. Suppose I have an urn with $w$ white balls and $b$ black balls. At each turn, I draw a ball, note its color, and retrieve a ball with the opposite color, paint it the drawn ball's…
Cam.Davidson.Pilon
  • 11,476
  • 5
  • 47
  • 75
2
votes
0 answers

Recursive bayesian prediction, which model to use?

Let's say that I have a set of random variables $X=\{X_1,..X_t,..X_T\}$ ($t$ is a time index). I know that every one of these random variables $X_t$ generate a multivariate Gaussian Distribution and are related somehow (for every $i$, $X_i$ and…
vphenix
  • 507
  • 2
  • 13
2
votes
1 answer

Asymptotic distribution of a recursive statistic

I have a (time series related) test statistic which is asymptotically normal. I would like to know what is the asymptotic distribution of its maximal value obtained by a recursive estimation. For example, lets say that I am estimating $$y_t=\beta…
icaspi
  • 23
  • 3
1
2 3