Inference, in a statistical context, refers to drawing conclusions from data containing an element of randomness introduced by e.g. measurement error, sampling variation, or assignment of experimental treatments. A common inferential paradigm is drawing conclusions about population parameters from ...

learn more… | top users | synonyms

1
vote
0answers
30 views

What is relationship between Fisher Information and Variance in natural exponential Family?

I know that $Var(\hat\theta)\geq 1/I(\theta)$ where $I(\theta)$ is Fisher information. Let take an example of natural exponential family with density $f(x)=\lambda\exp(-\lambda x)$. In this case we ...
1
vote
0answers
15 views

Score Confidence Interval for Difference of Proportions

In Agresti et al. (2008) "Simultaneous confidence intervals for comparing binomial parameter", it is suggested to use a studentized range distribution with a score statistic. However, exactly how you ...
1
vote
0answers
10 views

Is data fusion suitable for this application?

I have a situation where the delay vehicles experience at an intersection can be obtained through 3 or 4 types of sensors. Each sensor provides its own type of data with their own unique ...
1
vote
0answers
22 views

What criterion are used to accept/reject hypotheses in ridge regression?

On what basis might one accept/reject a hypothesis when running ridge regression? For example, if I have five predictor variables as part of a ridge regression... what criterion would I use to accept ...
0
votes
0answers
8 views

Why are chordal graphs special for inference in the context of Probabilistic graphical model?

I was trying to make a list of the reasons of why chordal graphs are important or interesting in the context of inference and probabilistic graphical models. Some of the reasons I have so far are: ...
1
vote
0answers
16 views

adaptive surveying for a maximal income that depends on a parameter

I have a product that I would like to price for the highest income. The income $I$ from this product will depend on the asking price $c$: $$ I(c) = N \cdot E(c)$$ where $E(c)$ is the expected ...
1
vote
1answer
22 views

Sampling: the % of homeowners who own at least 2 TVs

It is planned to conduct a study on the percentage of homeowners who have at least two TVs. What should be the sample size if we want to ensure that $95\%$ of estimation error is less than ...
8
votes
1answer
101 views

Observed Fisher information under a transformation

From "In All Likelihood: Statistical Modeling and Inference Using Likelihood" by Y. Pawitan, the likelihood of a re-parameterization $\theta\mapsto g(\theta)=\psi$ is defined as $$ ...
1
vote
1answer
39 views

Finding the unbiased variance estimator in high dimensional spaces

The problem comes from linear regression. Assume the regression function is linear, i.e. $$ f(X) = \beta_0+\sum_{j=1}^pX_j\beta_j $$ .Given a set of training data $(x_1, y_1),\ldots,(x_N,y_N)$,we try ...
13
votes
1answer
190 views

Invalid inference when observations are not independent

I learned in elementary statistics that, with a general linear model, for inferences to be valid, observations must be independent. When clustering occurs, independence may no longer hold leading to ...
6
votes
2answers
104 views

Example of an inconsistent Maximum likelihood estimator

I'm reading a comment to a paper, and the author states that sometimes, even though the estimators (found by ML or maximum quasilikelihood) may not be consistent, the power of a likelihood ratio or ...
0
votes
0answers
25 views

Inference methods for multimodality and label switching

Imagine that there are three professions in the world $a,b,c$ (astronauts, doctors and statisticians) and that the Gross Domestic Product (GDP) of a city can be modeled as a linear regression of its ...
2
votes
1answer
38 views

Sufficient and complete statistic

Let $ X_1, ... , X_n $ be i.i.d random variables with pdf given by $$f(x;\theta) = \exp(-(x-\theta))I_{(\theta, \infty)}(x)$$ It is asked to find a sufficient statistics for $ \theta $ and to verify ...
0
votes
0answers
16 views

Robust multivariate Wald test for significance in proportional odds model

I am using the rms package (Harrell) to estimate a proportional odds model to determine the association between an ordinal outcome (frequency of pain) and the ...
0
votes
1answer
70 views

LR test on marginal effect

Say I have the following regression model: $$\text{Wage}_i = constant + α·\text{YearsOfEduc}_i + β·\text{Age}_i + γ·\text{CompletedHighSchool}_i + \mbox{δ·$\text{NumOfSiblings}_i$} + ...
0
votes
1answer
38 views

Wald test on marginal effect

Say I have the following regression equation: $$Wage_i = YearsOfEduc_i + Age_i + NumOfSiblings_i + u_i$$ How would I go about peforming a wald test of the hypothesis that for an individual with ...
6
votes
0answers
91 views

Should we address multiple comparisons adjustments when using confidence intervals?

Suppose we have a multiple comparisons scenario like such as post hoc inference on pairwise statistics, or a multiple regression, where we are making a total of $m$ comparisons. Suppose also, that we ...
3
votes
0answers
62 views

Unbiased estimator with minimum variance for $1/\theta$

Let$ X_1, ...,X_n$ be a random sample feom a distribution $Geometric(\theta)$ for $0<\theta<1$. I.e, $$p_{\theta}(x)=\theta(1-\theta)^{x-1} I_{\{1,2,...\}}(x)$$ Find the unbiased estimator ...
1
vote
0answers
14 views

Detecting parameter influence

I have a data set consisting of a system's responses to various test configurations. Every test configuration corresponds to a different parameter set. These parameters can have either continuous ...
0
votes
0answers
9 views

Combining weighted evidence based probabilities?

I'm trying to identify people by determining if a data sample matches a set of existing samples (assume DNA if it helps). In addition to the samples I have a function which gives a probability that ...
2
votes
0answers
63 views

Inference of Pearson's rho from distribution perturbation

I would like to infer the correlation between random variables $Q$ and $R$, however, I have access only to the distribution of $Q$ and the distribution of $P=Q+R$. We can see how Pearson's $\rho$ ...
5
votes
2answers
95 views

Maximum likelihood estimator for $\theta$ and $E[X]$

Let $X_1,..., X_n $ be a random sample of a variable with PDF: $$f(x|\theta)=\frac{\theta}{x^2} I_{(\theta, \infty)}(x), \theta >0$$ Find the maximum likelihood estimator for $\theta$ and $ E[X]$ ...
2
votes
2answers
76 views

How can I calculate t-score without knowing true population mean?

I am studying now t-scores. As far as I understand, t-scores are used when we don't know true population parameters (such as: standard deviation and population mean) and cant use z-scores. Here is ...
1
vote
1answer
71 views

Bayesian inference of a clinical trial for clinicians

I am a clinician who is more adept than average at interpreting clinical trials in a frequentist manner. At this point, interpreting a trial as a frequentist has kind of become a procedure: check ...
1
vote
2answers
80 views

Inferring prior distribution

Suppose that we take a sample ($X_1, X_2, ... X_n$) from a distribution where we assume that $X_i $~$ Bin(n_i, p_i)$ and $n_i$ is known for every $i$. We also assume that $p_i$'s are independent and ...
0
votes
0answers
28 views

Proof for Sufficient and complete statistic (Shao)

Please can you help me, with this question: Let $X$ be a random variable with a distribution $P_\theta$ in $\{P_\theta : \theta \in \Theta\}$, $f_\theta$ be the p.d.f of $P_\theta$ w.r.t a measure ...
1
vote
1answer
50 views

Random Effect Model

One factor random effect model: $$y_{ij}=\mu+\tau_{i}+\epsilon_{ij}\quad i=1,2,\ldots,a; j=1,2,\ldots,n$$ where, $y_{ij}$ is the $j$th observation of $i$th treatment effect $\mu$ is the overall ...
2
votes
1answer
52 views

Unbiased estimator for $P(X_1=1)$

If $ X_1, ... ,X_n$ are IID binomial with parameters $ n$ and $p, $ find an unbiased estimator for $$G(p)=P(X_1=1)=np(1-p)^{n-1}\, .$$ I need to find this estimator so I can apply Lehmann-Scheffé ...
1
vote
0answers
32 views

Computing the F-ratio under null hypothesis

Statistical model for a Completely Randomized Design: $$y_{ij}=\mu+\tau_{i}+\epsilon_{ij}\quad i=1,2,\ldots,a; j=1,2,\ldots,n$$ where, $y_{ij}$ is the $j$th observation of $i$th treatment effect ...
0
votes
0answers
58 views

A doubt on the definition of p-value

The p-value is the probability, under the assumption of the null hypothesis $H_0$, of obtaining a result equal to or more extreme than what was observed at given data. This means, if I were to ...
2
votes
1answer
40 views

whether Y(employees injured) variation is due to X1(job function) or X2(population)

Here is the actual question- There are 1000 employees in a firm, and the firm has four departments namely D1, D2, D3 and D4 with 100, 200, 300, 400 employees respectively. Now, each employee is ...
3
votes
0answers
28 views

Is there a test/technique/method for comparing principal components decompositions between samples?

Is there any methodical way to compare the directions, magnitudes, etc of PCA results for different samples? I'm leaving the nature of the test deliberately vague because I'd like to hear all the ...
2
votes
1answer
47 views

If my normality test is non-significant, am I safe to use the t-test?

I took a 30 unit sample from a population. The sample distribution resulted to be normal. Can I state that the population distribution is normal too? If so, with what level of confidence?
2
votes
1answer
52 views

Does the parameter change during data generation in Bayesian Inference?

Let's assume that we have the following graphical model: This graph encodes the joint distribution $P(p,x_1,x_2,x_3,x_4) = P(p)\prod_{i=1}^{4}P(x_i|p)$. In the Bayesian inference, if we know ...
1
vote
1answer
46 views

Question about the Bayesian Inference of a parameter

In order to understand the difference between the Frequentist and Bayesian inference, I was reading the presentation at: http://www.stat.ufl.edu/archived/casella/Talks/BayesRefresher.pdf . In order to ...
3
votes
2answers
136 views

Normalization to non-degenerate distribution

I am reading de Haan's Extreme Value Theory (2006). In the discussion of distribution of sample maximum, he said "in order to obtain a non-degenerate limit distribution, a normalization is necessary". ...
2
votes
1answer
52 views

Show that a statistic is ancillary

Let $X_{i} \sim U(0, \theta) $ and $X=(X_1,\dots,X_n)$. Show that $$ \frac{X_{(1)}}{X_{(n)}}$$ Is ancillary for theta I coulxnt find a way of doing it that looks convenient. Any idea? P.s: ...
0
votes
0answers
27 views

Testing association between exposure and disease

In a particular example from the book Epidemiologic Research by Kleinbaum [example 15.1], I have three problems. Consider the data in table 01. These data pertain to a follow-up study concerning the ...
2
votes
2answers
67 views

Significant difference and correlations

Is it true that when there's no significant difference between groups then there will be correlations between groups? My situation is as follows: I have a sample that was measured using two ...
0
votes
1answer
51 views

How do you measure the accuracy of an inference hypothesis/procedure?

Take inference to mean reasoning/predicting the value of a hidden/laten variable $Z$ given some evidence/data $X$. For example, maybe you are trying to find out if your patient has Cancer (Z = 1 if he ...
1
vote
0answers
17 views

Two-tailed test [duplicate]

In one-tailed test , we give our decision at $\alpha$ level of significant. But in two-tailed test , why do we give our decision at $2\alpha$ level of significant? Why do we not give the decision of ...
3
votes
2answers
75 views

Estimating total number of people from an observed sample

The well known "German tank problem" shows how to answer the question: "If I have tanks which have an increasing serial number, and I see a sample of tanks and record their serial numbers, what is the ...
1
vote
1answer
20 views

Do I need to care about constants in Expectation Propagation

I am trying to approximate a certain factor in my graph. Following Tom Minka's tutorial what I have to do is as follows: $$ \prod_{i=1}^3 q_{w_i}(\pi_2)\approx \int ...
1
vote
0answers
87 views

Basic problem in Bayesian inference

I have questions with the following Bayesian inference problem I found in the book by Bertsekas & Tsitsiklis (Introduction to Probability 2nd ed.). Problem is as follows (P.445, Problem 2): ...
0
votes
1answer
41 views

Getting all zero correlations,$\rho_{ij}=\frac{\mathbb cov(e_i,e_j)}{(V(e_i)V(e_j))^{1/2}}$

Consider the general regression model $$Y=X\beta+\epsilon$$ where, $Y$ is an $(n\times 1)$ vector of observations, $X$ is an $(n\times p)$ matrix of known form, $\beta$ is a $(p\times 1)$ vector ...
2
votes
0answers
140 views

Estimating abundance using non-normal count data

I have sample counts of $n=20$ or $n=7$ taken from right-skewed and zero-inflated populations. The challenge in each case is to use the sample to estimate the total count in that population. Each of ...
2
votes
1answer
45 views

What is max-sum / max-product variant of loopy BP computing?

In (Nowazin and Lampert, Structured Learning and Prediction in Computer Vision, p. 29.), they say that in the max-sum variant of loopy belief propagation, the "variable max-beliefs are no longer ...
11
votes
3answers
543 views

Do the pdf and the pmf and the cdf contain the same information?

Do the pdf and the pmf and the cdf contain the same information? For me the pdf gives the whole probability to a certain point(basically the area under the probability). The pmf give the probability ...
2
votes
4answers
252 views

Can a trend stationary series be modeled with ARIMA?

I have a question / confusion about stationary series required for modeling with ARIMA(X). I am thinking of this more in terms of inference (effect of an intervention), but would like to know if ...
1
vote
1answer
44 views

Maximum likelihood estimator for variance in two linear models

I am learning MLE's at my inference class and this is a problem I came accross. Consider two simple linear models. $y_{1j}=\alpha _1+\beta_{1}x_{1j}+\epsilon_{1j}$ and $y_{2j}=\alpha ...