Questions tagged [bounds]
The bounds tag has no usage guidance.
211
questions
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0answers
20 views
Bounds on distance between two independently variables drawn from the same distribution
Suppose $X_1$ and $X_2$ are iid from an arbitrary distribution with variance $\sigma^2$. How can we derive an upper bound for:
$$P(|X_1-X_2|\ge\delta)$$
One simple idea is Chebyshev's Inequality, ...
2
votes
0answers
32 views
Are there supposed to be bounds on parameters in 2PL Item Response Theory models?
Recently I've been studying Item Response Theory (IRT) and have come across some issues with the application side of it. I currently have a dataset of ~200 respondents x 7405 questions (quite ...
1
vote
0answers
11 views
How to find upper and lower bound
Let $\Sigma \in S_{++}^n$ be a symmteric positive definte matrix with all diagonal entries one. Let $U \in R^{n \times k_1}$, $W \in R^{n \times k_2}$, $\Lambda \in R^{k_1 \times k_1}$ and $T \in R^{...
0
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0answers
24 views
Bounding the norm of the difference between two related probability densities
Suppose we have a continuous random variable $X$ and two continuous functions $f$ and $g$ such that $f(X)$ and $g(X)$ are continuous random variables. Let $p_A$ be the probability density function of ...
3
votes
2answers
218 views
Is there a statistical distribution whose values are bounded $[-1,1]$ and sum to 1?
The Dirichlet distribution contains values that are bounded $[0,1]\in \mathbb{R}$ and sum to $1$. Is there a parametric distribution or similar method whose values do the same but reach as low as $-1$?...
0
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0answers
26 views
How to get this bound?
I read the following part in a paper, it is trying to show that the difference between $g(x,\gamma)$ and its linearized version is small. Here $g(z,\gamma)$ depends on two generic functions $\gamma=(\...
0
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0answers
20 views
What is a common-sensical approach to setting the boundaries of an interval?
As I am trying to present my results to a non-expert audience, I am wondering about what the most commonly used boundaries are for intervals. I mean specifically, which of the four versions explained ...
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0answers
40 views
How to bound a regressor function?
I've seen similar questions on here, but none seem to quite apply to my use case.
I want to predict Metacritic scores bases on a number of features. Metacritic scores are bounded to a 0-100 scale, ...
1
vote
1answer
44 views
Use Chebyshev's inequality to ļ¬nd a lower bound of a Chi-Square Distribution
I'm trying to solve the following exercise but I'm not sure if what I'm doing is right.
"Let $X$ be an r.v. distributed as $\chi_{40}^{2}$. Use Tchebichevās inequality
in order to ļ¬nd a lower ...
-2
votes
1answer
53 views
Positive or negatively bounded CDFs [closed]
If $X\in\mathbb{R}^n$ is a continuous random variable whose cumulative distribution function is ordinarily
$$F_X(x) = \int_{-\infty}^{\infty} f_X(x) dx $$
what is the meaning of
$$F_X(x) = \int_{0}^{\...
1
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0answers
29 views
Does a generalization bound that holds with high probability imply a bound that holds in expectation?
I am interested in generalization bounds, for example PAC bounds (Probably Approximately Correct). In particular, I wonder if a high probability bound implies a bound in expectation (or vice versa).
...
1
vote
1answer
26 views
How to deal with training models on data where the examples are highly dependent on each other?
Say you have a dataset of products sold at a store with the special condition that each day there is only one of each product in stock. That is, if there are multiple orders for a given product on a ...
2
votes
1answer
26 views
A tail bound for an unknown distribution via sampling
I know that many results exist for making an argument about the tail of a distribution, i.e., for a random variable $X$, one can find a bound $\epsilon$ such that $\Pr[X \geq a]<\epsilon$. Some ...
2
votes
1answer
36 views
Is a bounded real-number random variable discrete or continuous?
A discrete random variable is countable (such as integers and natural numbers), whereas a continuous r.v. is not countable (like the real numbers $\mathbb{R}$).
If I have a dataset whose observations ...
2
votes
4answers
292 views
How to generate random numbers normally distributed in R or any software with limitations (bounds)?
I am working on a project where I need to generate random numbers for a given task time which is normally distributed with mean = 40, and standard deviation = 150.
Because of the high SD, I will get ...
6
votes
1answer
72 views
On the difference between the main effect in a one-factor and a two-factor regression
Consider a linear regression (based on least squares) on two predictors including an interaction term: $$Y=(b_0+b_1X_1)+(b_2+b_3X_1)X_2$$
$b_2$ here corresponds to the conditional effect of $X_2$ when ...
1
vote
1answer
24 views
Cramer-Rao Lower Bound Proof (fuzzy step)
The following is the derivation of the Cramer-Rao lower bound as detailed on p.336 of Casella and Berger's Statistical Inference:
$\frac{d}{d\theta}E[W(\bf{X})|\theta] = \int_{\chi}W(\bf{x})\left[\...
0
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0answers
16 views
Suppose $max\{a_i\}_{i=1}^{Rn}\overset{p}{\rightarrow} a_0$, where $a_i$ are i.i.d.r.v.. Are there any results on its rate of convergence?
Suppose $max\{a_i\}_{i=1}^{Rn}\overset{p}{\rightarrow} a_0$, where $a_i$ are i.i.d. random variables, $a_0$ is a constant and $R_n\rightarrow\infty$ as $n\rightarrow\infty$.
Are there any results on ...
0
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0answers
19 views
Symmetrization in Proof of Hoeffding's Lemma
This alternative proof of a slightly weaker version of Hoeffding's Lemma features in Stanford's CS229 course notes. What's notable about this proof is its use of symmetrization. However, I find this ...
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0answers
7 views
What's a good way to select a bound that's close to zero?
I have a bunch of position data that I transformed into speed data. I'm assuming that I have some noise in my data and that the noise got worse after transforming to speed. I used a Kalman filter to ...
2
votes
1answer
21 views
Why $Pr[X-\mu \geq t]= Pr[e^{\lambda(X-\mu)} \geq e^{\lambda t}]$ for all $\lambda> 0$
I hope everyone is having a nice day. I don't know why this inequality holds.
$$
Pr[X-\mu \geq t]= Pr[e^{\lambda(X-\mu)} \geq e^{\lambda t}]
$$
For $\lambda >0$. I guess it has something to do ...
0
votes
1answer
41 views
Does the 1-Wasserstein distance have an upper and a lower bound?
Given $u$ and $v$ two probability distributions and U and V their respective $CDFs$, the $1$-Wasserstein distance is formulated as follows:
$l_1(u,v)=\int_{-\infty}^{+\infty}|U-V|$
Does $l_1$ have ...
0
votes
0answers
30 views
Equivalence testing: Is it appropriate to set the equivalence bound such that I can reject H0 at alpha=0.05?
I have conducted a survey. One sample answered a binary question (answer A or B), once with and once without treatment. Now there does not seem to be a treatment effect as the proportions of answers ...
1
vote
0answers
19 views
Causal AR Model?
This questions is about necessary conditions (in form of inequality on coefficients) for the causality of autoregressive models. For instance, $|\phi_1| < 1$ is a necessary condition for an AR(1) ...
1
vote
0answers
17 views
How to derive this MAE error bound on the central limit theorem?
Is this derived from Chebyshev's inequality or a tail bound theorem? If not, how was it derived?
Does this require the existence of the third moment?
Does this bound suggest the normal approximation ...
0
votes
0answers
24 views
Factorial moment bound for discrete Binomial distribution
I need to compute the upped bound for the tail (survivor) probability $P(X \ge t)$ for the discrete Binomial random variable $X$. I could use Chernoff bounds, however according to this paper [1] the ...
0
votes
0answers
11 views
Theoretical lower limit of area contained in 1 sigma interval of a unimodal distribution
It is known that in case of a normal distribution, the interval of one standard deviation around the mean, $\mu \pm 1\sigma$, contains about $68\%$ of the data.
When considering an arbitrary ...
0
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0answers
11 views
Occam Bound using Relative Chernoff Bound
I'm having a bit of a trouble trying to understand one step in the proof of an Occam Bound (Theorem 1) in the paper "A PAC-Bayesian Tutorial with A Dropout Bound" (https://arxiv.org/pdf/1307.2118.pdf)
...
0
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0answers
22 views
Choosing constants for probabilistic bounds
I am studying probabilistic bounds and I have a question regarding how to choose constants from complexity classes.
Specifically, consider a biased coin which has the probability of one side $p = \...
1
vote
1answer
28 views
How to using the Markov Inequality to find the upper bound for $\mathbb{P}(X > 2)$ given I only have information about $X^4$?
Let $X$ be a nonnegative random variable that satisfies $\mathbb{E}[X^{4}]=4$ .
How should I calculate an estimate for the $\mathbb{P}(X \geq 2)$ using the Markov Inequality?
I tried to find a ...
0
votes
2answers
56 views
Which Distribution functions with increasing hazard rate has x(1-F(x)) tending to 0 when x tends to infinity?
Let $F(x)$ be a cumulated distribution function and $f(x)$ the probability density function with an increasing failure rate (IFR or hazard rate), ie $h(z)=f(x)/(1-F(x))$ is increasing. Which ...
2
votes
1answer
48 views
Bounding the structural-risk-minimization (using Hoeffding's inequality twice)
tl;dr:
The main question is if I use an inequality that is true with a certain probability (confidence) twice, do I get the same confidence?
Original:
I've got the following exercise:
Where $e_p(h)...
0
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0answers
10 views
Bounded Model Prediction Error
I have a predictive model (not ML based, uses first principles from a science textbook) and I would like to have a confident bound on on the error of the predictions. I am able to collect many ...
2
votes
0answers
24 views
Rigid regression - show $||w||_2$ is $O(\lambda ^ -1)$
Relevant question: Ridge regression formulation as constrained versus penalized: How are they equivalent?
I've got an assignment to show that in rigid regression the coefficients vector $L_2 $ norm, $|...
3
votes
1answer
106 views
Can you bound the third moment from the second moment?
Suppose $X$ is a random real variable with zero mean and finite second moment $\langle X^2\rangle$. Under what conditions can we give a bound (upper/lower) for the third moment $\langle X^3\rangle$?
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27 views
Are the following terminologies error/risk/marmgin/regret bounds related?
I recently come across papers with titles resembling "Error/Risk/Margin/Regret Bounds" and I can't help but wondering if there is any fundamental (mathematical) difference between these terminologies?
...
4
votes
1answer
34 views
How to compare the distributions of censored data?
Is there a way to test if the distributions of the two samples of censored data? As the data is not defined exactly, Kolmogorov-Smirnov test does not seem to be directly applicable.
Generally ...
1
vote
0answers
14 views
markov's inequality generalizability
Let $X : \Omega \rightarrow \mathbb{R}$ be a non-negative random
variable on probability space $(\Omega, \mathscr{A}, P)$ and let $c >
0$. Then: $$\mathrm{P}[X > c] \leq \frac{\mathbb{E}(X)}{...
0
votes
1answer
59 views
the approximation of the variance of MLE (Cramer-Rai Lower Bound)
This is in In Casella's Statistical Inferenceļ¼page 473, the approximation of the variance of MLE (Cramer-Rao Lower Bound). I really confused with the conclusion:
$Var_{\hat{\theta}}h(\hat{\theta})$ ...
0
votes
0answers
22 views
Lower bound for regression task
I have a dataset $\mathbf{X} = \{ \mathbf{x_1},\mathbf{x_2},...,\mathbf{x_n} \}, x\in\mathcal{R}$ with length $n$ and dimension $d$ along with corresponding labels $\mathbf{y}, y \in \mathcal{R}^+$.
...
3
votes
1answer
55 views
Upper Bound on the Wasserstein Distance
I'm interested to know if it's possible to construct an upper bound on the Wasserstein distance in terms of the Kolgomorov distance.
The Wasserstein distance can we written as
$$W_{1}\left(F, G\...
1
vote
1answer
53 views
Fourth moment bound for unit-variance distribution
Given that a random real variable $X$ has zero mean and variance equal to 1, can we bound its fourth moment $\langle X^4\rangle$ (assuming it exists)?
1
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0answers
34 views
Comparing numerical stability and computing bounds on the condition number of learned weights
I have an empirical risk minimization problem with two equivalent losses that solves it, $f_1(x; \theta_1)$ and $f_2(x ; \theta_2)$, where $x$ is the data and $\theta$ are the model parameters (in ...
0
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0answers
19 views
Exact inference in an approximate model as opposed to approximate inference in an exact model?
I remember hearing a while ago that it was more rigorous to perform approximate inference in an exact model as opposed to exact inference in an approximate model. I canāt now remember the reasoning ...
0
votes
1answer
42 views
Changing only one point of a discrete distribution to maximize variance augmentation
X has a discrete distribution with support $x1, x2, ...$ in $ {]}0,1{[}$. You have the right to change only one of the $xi$ to lead to the highest increase in variance (or, at least, a systematic ...
0
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0answers
37 views
Lower bound/ upper bound of standard deviation
The attached screenshot is from the 2 page of following publication
Dissolution test results.
The upper bound for standard deviation is calculated in cell B7. The formula for the calculation is also ...
0
votes
0answers
15 views
Bound on sample size- Hoeffdings inequality
Studying for my upcoming statistics exam I tried to solve the following:
In some population, each individual likes exactly one out of 30 possible music genres. In some survey, n people are drawn ...
0
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0answers
19 views
Sampling a proposed value with a limited range target when running MCMC [duplicate]
I want to do an MCMC algorithm and need to sample a proposed value from a proposed distribution. In the Metropolis algorithm, people usually use a normal distribution as proposal. But if the prior ...
1
vote
1answer
19 views
Is a range of values from an exponential distribution still exponentially distributed?
I have to generate numbers of two different exponential distribution ($e_1, e_2$) with parameters respectively $\lambda_1$ and $\lambda_2 = k \lambda_1$, with $0<k<1$.
But I also want to ...
2
votes
0answers
15 views
Rate of convergence of variance of kernel averages
I'm reading Hansen's (2008, p. 729) Theorem 1 where he bounds the variance of averages of the form
$$\hat\Psi(x)=\frac{1}{Th}\sum_{t=1}^T Y_t K\bigg(\frac{x-X_t}{h}\bigg)$$
given that $\{(Y_t,X_t)\}_{...
