Chapter 6 Importance sampling

Joint and Marginal Distributions - University of Arizona

Joint and Marginal Distributions October 23, 2008 We will now consider more than one random variable at a time. As we shall see, developing the theory of multivariate distributions will allow us to consider situations that model the actual collection of data and form the foundation of inference based on those data. 1 Discrete Random Variables

  Marginal

1 Sufficient statistics

conditional distribution. But then his random sample has the same distri-bution as a random sample drawn from the population (with its unknown value of θ). So statistician B can use his random sample X0 1,···,X0 n to com-pute whatever statistician A computes using his random sample X1,···,Xn, and he will (on average) do as well as ...

  Random, Conditional

A Conditional expectation

The partition theorem says that if Bn is a partition of the sample space then E[X] = X n E[XjBn]P(Bn) Now suppose that X and Y are discrete RV’s. If y is in the range of Y then Y = y is a event with nonzero probability, so we can use it as the B in the above.

Method of Moments - University of Arizona

The muon is an elementary particle with an electric charge of 1 and a spin (an intrinsic angular momentum) of 1/2. It is an unstable subatomic particle with a mean lifetime of 2.2 µs. Muons have a mass of about 200 times the mass of an electron. Since the muon’s charge and spin are the same as the electron, a muon can be

  Methods, Moment, Muon, Method of moments

Topic 15 Maximum Likelihood Estimation

Maximum Likelihood Estimation Multidimensional Estimation 1/10. Fisher Information Example Outline Fisher Information Example Distribution of Fitness E ects ... To obtain the maximum likelihood estimate for the gamma family of random variables, write the likelihood L( ; jx) = ( ) x 1 1 e x1 ( ) x 1 n e xn = ( ) n (x 1x 2 x

  Maximum, Estimation, Likelihood, Maximum likelihood estimation, Maximum likelihood

Maximum Likelihood Estimation - University of Arizona

Introduction to the Science of Statistics Maximum Likelihood Estimation 0.2 0.3 0.4 0.5 0.6 0.7 0.0e+00 5.0e-07 1.0e-06 1.5e-06 p l 0.2 0.3 0.4 0.5 0.6 0.7

  Estimation

Topic 15: Maximum Likelihood Estimation

Introduction to Statistical Methodology Maximum Likelihood Estimation Exercise 3. Check that this is a maximum. Thus, p^(x) = x: In this case the maximum likelihood estimator is also unbiased. Example 4 (Normal data). Maximum likelihood estimation can be applied to a vector valued parameter. For a simple

  Topics, Maximum, Estimation, Likelihood, Maximum likelihood estimation, Topic 15

Innovative Methods of Teaching - University of Arizona

The reason being that martyr is engaged in defense work while an alim (scholar) builds individuals and nations along positive lines. In this way he bestows a real life to the world. “Education is the manifestation of perfection already in man” – (Swami Vivekananda)

  Martyr

Probability Theory - University of Arizona

Probability Theory December 12, 2006 Contents 1 Probability Measures, Random Variables, and Expectation 3 ... Definition 1.18. Let f : (S,S) → (T,T ) be a function between measure spaces, then f is called measurable if f−1(B) ∈ S for every B ∈ T . (1.6) If (S,S) has a probability measure, then f is called a random variable.

  Measure, Theory, Space, Measurable, Measure spaces

Interval Estimation - University of Arizona

likelihood, and evaluate the quality of the estimator by evaluating the bias and the variance of the estimator. Often, we know more about the distribution of the estimator and this allows us to take a more comprehensive statement about the estimation procedure. Interval estimation is an alternative to the variety of techniques we have examined.

  Estimation, Likelihood

Designing a Statistically Sound Sampling Plan

Sampling Plans zSimple Random Sample zEach sampling unit has an equal probability of being sampled with each selection. zCan perform simple random sampling if: zEnumerate every unit of the population zRandomly select n of the numbers and the sample consists of the units with those IDs zOne way to do this is to use a random number table or random number generator

  Sampling

“Audit Sampling 101”

What is sampling and when is it used? Audit / Sampling / Non-Sampling Risks Statistical vs. Non-Statistical Sampling Attribute / Variable Sampling Steps in the sampling process Terms used in sample planning Terms used in evaluating results Computer Assisted Auditing Techniques Questions

  Audit, Variable, Sampling, Audit sampling 101, Variable sampling

Importance Sampling - Statistics

Importance Sampling The methods we’ve introduced so far generate arbitrary points from a distribution to ap- ... This method is only considered reliable when the weights are not too variable. As a rule of thumb, when ESS = v u u t1 N XN i=1 w˜(X i) w −1 2 is less than 5, this method is reasonable. Here w is the sample mean of the ˜w(X

  Variable, Sampling

Sampling and Sample Size Determination

1.Identify variable(s) as an efficient basis for stratification. Must be known to be related to dependent variable. Usually a categorical variable 2.Complete list of population elements must be obtained 3.Use randomization to take a simple random sample from each stratum

  Variable, Sampling

Soil Sampling Guidelines - Purdue University

Soil sampling for spatially explicit management There are two main methods for soil sampling in spatially explicit management — zone-based sampling and grid sampling. With each method, soil samples are collected from predefined areas in a field. By correlating the soil test results with the area of the Figure 3.

  Guidelines, Soil, Sampling, Soil sampling guidelines

Title stata.com bootstrap — Bootstrap sampling and estimation

idcluster(newvar) creates a new variable containing a unique identifier for each resampled cluster. This option requires that cluster() also be specified. saving(filename, suboptions) creates a Stata data file (.dta file) consisting of (for each statistic in exp list) a variable containing the replicates.

  Variable, Sampling

Chapter 4 - Stratified Random Sampling

stratified random sampling. The number of samples selected from each stratum is proportional to the size, variation, as well as the cost (c i) of sampling in each stratum. More sampling effort is allocated to larger and more variable strata, and less to strata that are more costly to sample

  Variable, Sampling

Two-Stage Least Squares Estimation of Average Causal ...

the average causal effect of variable treatments such as drug dosage, hours of exam preparation, cigarette smoking, and years of schooling. The average causal effect in which we are interested is a conditional expectation of the difference between the outcomes ... sampling variance of IV estimates of treatment effects. In another recent paper ...

  Variable, Sampling

AP Statistics: Unit – 5 - Sampling Distributions Practice Test

Question 7 A statistic is an unbiased estimator of a parameter when… A.the statistic is calculated from a random sample. B.in many samples, the values of the statistic are very close to the value of the parameter. C. in a single sample, the value of the statistic is equal to the value of the parameter D.in many samples, the values of the statistic are centered at the value of the

  Sampling

Chapter 8: Quantitative Sampling

d. Snowball Sampling i. Snowball sampling (also called network, chain referral, or reputational sampling) is a method for identifying and sampling the cases in a network. It begins with one or a few people or cases and spreads out on the basis of …

  Sampling