Estimation theory is a branch of statistics and signal processing that deals with estimating the values of parameters based on measured/empirical data that has a random component.

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44 views

Prove that $\int k(w)o(h^2w^2)dw=o(h^2)$ for $\int k(w)dw=1$

Suppose that $k$ is nonnegative real-valued function satisfying $$ \int k(w)dw=1,\quad\int wk(w)dw=0,\quad\int w^2k(w)dw=\kappa_2<\infty.\tag{$\star$} $$ (The limits of the integrals are all ...
2
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1answer
51 views

Monotonicity of Sample Mean

$X_1,X_2,\ldots$ are drawn i.i.d. from a distribution with mean $\mu$. Define $\bar{X}_n = \frac{1}{n}\sum_{i=1}^n X_i$ Prove that $\forall t \quad E[|\bar{X}_t - \mu|] \geq E[|\bar{X}_{t+1} - \mu|]$
2
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1answer
118 views

Finding the MLE of $\theta$ where $\theta \leq x$

consider the following PDF $$ \begin{eqnarray} f(x;\theta) &=& \left\{\begin{array}{ll} 2\frac{\theta^2}{x^3} & \theta \leqslant x\\ 0 & x< \theta; 0 < \theta \end{array}\right.\...
2
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2answers
95 views

Have spd $(A^TA)$ and $(B^TB)$, need $A^TB$.

Given two symmetric positive definite matrices $(A^TA)$ and $(B^TB)$ I need to compute $A^TB$. $A$ and $B$ are not given directly. $(A^TA)$ and $(B^TB)$ have the same dimensions. $A$ and $B$ are ...
2
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2answers
1k views

Some expectation values for a Gamma distribution

Assuming I have a Gamma distributed random Variable $x \sim Gamma( \alpha, \beta )$. Now I like to have the following two expectation values (integrals): $E \left[ x \ln x \right]$ $E \left[ \ln \...
2
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1answer
44 views

Maximum Likelihood of single observation

I'm stumped on this problem... I keep getting an undefined answer of having to solve -20 = 0. The likelihood function I get is $e^{-20\alpha}$. So I have $y_i=$ $ \begin{cases} 1& w/...
2
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1answer
88 views

Expectation of largest and smallest order statistic from uniform distribution

Given is a random sample of size n from a uniform distribution with parameters $-\theta$ and $\theta$, $\theta>0$. I'm asked to find a constant $c$ such that $c(X_{n:n}-X_{1:n})$ is an unbiased ...
2
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1answer
319 views

Bias, SE and MSE of Uniform Distribution

Let $X_1,\ldots,X_n$ be an i.i.d. sequence of Uniform $(\mu,2\mu)$ and let an estimator be $\hat{\mu} = \frac{1}{2} \max\{X_1,\ldots,X_n\}$. Find the bias, SE, and MSE of this estimator. Hint: Let $...
2
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1answer
200 views

Hypothesis test between two normal distributions

Let $T_1,T_2,\ldots ,T_ n$ be i.i.d. observations, each drawn from a common normal distribution with mean zero. With probability $1/2$ this normal distribution has variance $1$, and with probability $...
2
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1answer
231 views

UMVUE for pdf $f_{\theta}(x) = \theta e^{-\theta x}, x>0$

Let $X_1,\ldots,X_n$ be a random sample from a pdf $f_{\theta}(x) = \begin{cases} \theta e^{-\theta x}, & x>0 \\ 0, & \text{otherwise} \end{cases}$, where $\theta>0$ is an unknown ...
2
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1answer
124 views

When to use Central Limit Theorem or Cramers Theorem

In for example this paper the authors say The central limit theorem provides an estimate of the probability \begin{align} P\left( \frac{\sum_{i=1}^n X_i - n\mu}{\sigma \sqrt{n}} > x \right) \...
2
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1answer
455 views

Numerical calculation of fisher information

I am trying to obtain numerically the fisher information. Given a likelihood function $$ f(X,\theta),$$ with $X \in [0,1]$. The fisher information is given by $$ \mathbb{I}(\theta)=\mathbb{E}\left[\...
2
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2answers
773 views

Expected value of a max

We have a roulette with the circumference $a$. We spin the roulette 10 times and we measure 10 distances, $x_1,\ldots,x_{10}$, from a predefined zero-point. We can assume that those distances are $U(0,...
2
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0answers
25 views

Find an estimator by using the method of moment

Let $X$ be a discrete random variable with density function: $$p(x;\theta)=\left(\frac{\theta}{2}\right)^{\lvert x\rvert}(1-\theta)^{1-\lvert x\rvert}$$ where $x\in\{-1,0,1\}$ and $\theta \in[0,1]...
2
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1answer
25 views

Definition of Bias for Baysian Estimation

I know for parameter estimation the estimator $ \hat{\theta}(X)$ of $\theta$ based on observation $\theta$ is said to be unbiased if \begin{align} E[ \hat{\theta}(X)]=\theta, \ \forall \theta. \end{...
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26 views

Finding minimum energy graph, subject to constraints

I imagine there's a known algorithm for this, but am not totally sure what to search for, and so my search didn't turn up much. Basically, I have have a set of $N$ nodes $\hat x_i $ in a graph $\hat ...
2
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2answers
42 views

Is “non-random parameter estimation” the same thing as maximum likelihood estimation?

In one book and a few papers, mostly on navigational tracking, I have found reference to the method of "non-random parameter estimation" but this term is not on the Wikipedia and not in a lot of ...
2
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1answer
55 views

What is the concentration result of the entropy?

Let $X_1, X_2, \ldots, X_n$ be i.i.d. binary variables with $Pr(X_i=1)=p$ and $Pr(X_i=0)=1-p$. The famous result about $p$ is $$Pr\left(\left|\frac{1}{n}\sum_{i=1}^n X_i-p\right|>\epsilon\right)\...
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43 views

Nice applications of estimation theory and hypothesis testing

As a mathematics professor in an engineeer school, I want to write some lab work for students in Statistics. This work should last four hours and will be made in a language such as Matlab or Python. ...
2
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1answer
79 views

minimum kullback leibler estimator

Suppose that one has independent and identically distributed samples $x_i,i=1,...,n$ from some unknown density and one wants to fit a probability distribution $f_\theta(x)$, where $\theta$ is a (...
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31 views

MLE of two-dimensional distribution

Let $X_1, ..., X_n$ be a random sample from a continuous distribution with pdf $$f_{\theta,\kappa}(x) = \frac{\kappa\theta^\kappa}{x^{\kappa+1}}, x\geq \theta, \theta > 0, \kappa > 0.$$ How to ...
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77 views

Near-Application of Cauchy-Schwarz Inequality

I have the following situation: I have two estimators of $\alpha$, both via maximum likelihood of the density: $$ f(x,y\mid \alpha,\beta) = f(y \mid x,\alpha,\beta)f(x \mid \alpha) $$ One uses only ...
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43 views

Fisher Expected Information for a Gaussian Process model

Suppose I have a two dimensional Gaussian process model (GP), defined by a squared exponential correlation function s.t: $$R(x_{i},x_{j}) = \exp\left(-\frac{|x_{i} - x_{j}|^2}{2}\right).$$ I am ...
2
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0answers
29 views

ML estimation for Weibull

What are the maximum likelihood estimators of $\eta$ and $\beta$ ($\eta>0$ and $\beta>0$) for an i.i.d. sample of size $n$ from the following density: $f(y_i)=\frac{\beta x_i^{\beta-1} }{\eta ^ {...
2
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83 views

Simulation Velocity of a harmonic oscillator system

I am write a simulation for get true Velocity of a harmonic oscillator system as Where P=[p1 p2;p2 p3] can find using Rung-Kutta Integration method with P(0)=[1 0; 0 1] This is code to find p Now, ...
2
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55 views

Risk in density estimation: grasping the definition

When generalizing estimators to an entire function what is the space in which we perform the integral to obtain the expected value (with respect to this function)? For example, when estimating ...
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30 views

Estimation of Linear Projection

Given a linear system: $Y=AX+W$ Where: $X$ is the input signal of size $N \times K$ $Y$ is the output signal of size $M \times K$ $A$ is a projection of size $M\times N$; with $M >> N$ $W$ ...
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153 views

Polluting an image with Gaussian anisotropic noise and estimate the covariance matrix

Assume that we have a $d\times d$ grey-scale image represented as a vector $$ \mathbf{x}=(x_1,\ldots,x_D)^T\in[0,255]^D, $$ where $D=d\times d$. We would like to import some noise concerning the ...
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58 views

paramter estimation (maximum likelihood) of a mixture density

I have this mixture distribution $f(x) =w \cdot \mathcal{LN}(\mu_1,\sigma) + (1-w)\cdot \mathcal{LN}(\mu_2,\sigma) $ where $\mathcal{LN}(\mu,\sigma)$ is a lognormal distribution. I now have random ...
2
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1answer
42 views

Bayes - dual estimation of parameter value and parameter growth

I am trying to find an bayesian approach to the following problem: Image a bucket with 100 white balls and an unknown number of red balls During each year, one can take a sample with replacement of ...
2
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1answer
91 views

How to compute the unique MLE from an Exponential Family of Distributions?

Let $$ f(x;\theta)=\frac{1}{\pi} \frac{e^{\theta x}\cos(\theta \pi/2)}{\cosh(x)}, x\in{\mathbb{R}} $$ be a family of densities and which is clearly exponential family. Then what is the Maximum ...
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85 views

Identification of real functions

this my second question, so I'm still new... thanks in advance for any help! Basically, I'm looking for some references and tools to study the following problem. Consider the following function $f(\...
2
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98 views

Unbiased estimator with conditional expectation.

Suppose that $X$ has a binomial distribution with parameter $N=1$ and $p=1/2$. Y, which is independent of $X$, has a normal distribution with mean $\mu$ and variance 1. Consider the estimator $\mu$ of ...
2
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1answer
27 views

How to show this estimation?

i have this polynom $$p(x) = \sum_{i=0}^{m}a_ix^i$$ I want to show, that if $\tilde{z}$ is the approximation to the simple zero digit $z \neq 0$ in first approximation, the following estimation ...
2
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2answers
127 views

Why we always put log() before the joint pdf when we use MLE(Maximum likelihood Estimation)?

Maybe this question is simple, but I really need some help. When we use the Maximum Likelihood Estimation(MLE) to estimate the parameters, why we always put the log() before the joint density? To use ...
2
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0answers
58 views

What are the properties of median-unbiased estimators?

On Wikipedia it says that " A median-unbiased estimator minimizes the risk with respect to the absolute-deviation loss function, as observed by Laplace." How to prove this? Note that I asked on Cross ...
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0answers
228 views

Fisher Information and minimum variance estimators

I am trying to understand what can be proved about minimum variance estimators. I have changed the question to make it more specific. Let us assume we have some finite set $S$ of elements and we just ...
2
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1answer
196 views

The fundamental gaussian identities of bayesian estimation

In bayesian estimation, when the model and plant noise is hold , the optimal estimator is Kalman filter. but I am wondering is there any literature that could prove the following gaussian identities? ...
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0answers
103 views

Likelihood Function of Random Process

Given the following data: $$ x(t) = A + \omega(t) $$ where $ \omega(t) $ is an AWGN with zero mean, what would be likelihood function $p(x(t);A)$? I know it could be proven to be: $$ p(x;A) = C ...
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vote
2answers
144 views

If $(X_i)$ are i.i.d. exponential $\lambda$, then $\hat\lambda=n/\sum{X_i}$ is a biased estimator of $\lambda$

the main problem is that i have no clue on calculating $E(\frac{1}{x})$ let $U = \frac{1}{\frac{1}{n}\sum{X_i}}$ then, $E(U) = n*E(\frac{1}{\sum{X_i}})$. I think that i'm supposed to calculate: $\...
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130 views

Estimation theory: Finding MSE and Variance of an Estimator

Let $\hat{A}$ be an estimator of $A$ where $a<A<b$ and $\tilde{A}$ be another estimator such that$$\tilde{A} = \cases{a \text{ if } \hat A \leq a\\\hat A \text{ if } a< \hat A <b\\{b \text{...
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1answer
310 views

Sufficient Estimators and Generalized Likelihood Ratios

If you can make the assumption that a sufficient statistic exists for some parameter - let's call it $\theta$. How would you show that the critical region of a likelihood ratio test will depend on ...
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2answers
36 views

Asymptotically unbiased estimator for 1/p in Bernouilli distribution?

Suppose I have a sample of $n$ independent stochastic variables, each Bernouilli distributed with parameter $p$ (you may assume $0 < p <1$). I was wondering if there exist (asymptotically) ...
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2answers
434 views

Maximum a Posteriori (MAP) Estimator of Exponential Random Variable with Uniform Prior

What would be the Maximum a Posteriori (MAP) estimator for $ \lambda $ for IID $ \left\{ {x}_{i} \right\}_{i = 1}^{N} $ where $ {x}_{i} \sim \exp \left( \lambda \right), \; \lambda \sim U \left[ {u}_{...
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1answer
71 views

Linear Regression with independent but non-identical noise

If I have this linear regression equation: $$y=X\beta+\epsilon $$ ($x$ and $\beta$ are vectors) The likelihood function can be written as $$L= \prod_{n=1}^N N(y_n ;x_n ,\beta ,\sigma^2)=(2\pi \...
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1answer
92 views

Order related to Empirical distribution function and Normal distribution

Let $X_1,\dots,X_n$ are i.i.d with distribution function $F$. Let $\hat F_n$ be its empirical distribution function, i.e., $$ \hat F_n(x)=\frac1n\sum_{i=1}^n1_{\{X_\le x\}}(x) $$ where $1_A(x)$ is the ...
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2answers
3k views

Show that estimates are unbiased

The following is a problem in my book that I don't really understand: We take a random sample: $x_1,x_2,\ldots,x_n$ from a population that is $N(μ,σ)$ where $\mu$ and $\sigma$ are unknown. We build ...
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2answers
509 views

Question about unbiased estimator

$X_1,X_2,\ldots,X_n$ are iid random variables $B(1,\theta)$ where $0< \theta<1$. Let $w = 1$ if $\sum_i X_i = n$ and $0$ otherwise. What is the best unbiased estimator of $\theta^2$. Attempt: ...
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2answers
85 views

Is there an iterative way to evaluate least squares estimation?

Suppose to have a set of data $\{y_i, u_i\}_{i=1}^m$, where $y_i \in \mathbb{R}$ and $u_i \in \mathbb{R}^n$. The claim is that $$y_i = u_i^\top \theta + \varepsilon$$ where $\theta \in \mathbb{R}^n$...
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1answer
35 views

Improving estimatied parameters of a known distribution

Assume there is a Set of data which follows a known distribution (e.g. normal distribution). $$S = \left\{ a_0,a_1 ... a_n \right\}$$ When taking a subset from S $$S_k = \left\{ a_0,a_1 ... a_k \...