Empirical
Likelihood
Art B. Owen
CHAPMAN & HALL/CRC
Boca Raton London New York Washington, D.C.
2001 CRC Press LLC
disclaimer Page 1 Tuesday, April 24, 2001 10:57 AM
Library of Congress Cataloging-in-Publication Data
Owen, Art. (Art B.)
Empirical likeliho
Nonparametric Regression
Badr Missaoui
Nonparametric Regression
Outline
I
Kernel and local polynomial regression.
I
Penalized regression.
Nonparametric Regression
I
We are given n pairs of observations (X1 , Y1 ),.,(Xn , Yn )
where
Yi = r (Xi ) + i , i =
Part VI: Nonparametric Density Estimation
April 3, 2009
Introduction
Distribution of a continuous r.v. is characterized by a density h; in
applications: unknown
parametric models (normal distribution, exponential distribution):
estimation of the density e
SDASA One-Day Conference
Best Practices in Statistical Consulting
October 16, 2013
Colleen Kelly, Ph.D.
Principal Statistician
What are the attributes of the ideal
consultant?
Condensed from the list prepared by the ASA Committee to
examine the training n
Statistical Consultancy
David J. Hand
Imperial College
and
Winton Capital Management
_
Imperial College, London
1
Statistical consulting requires skills and expertise
completely distinct from academic and technical
expertise
90% of UK academic statisticia
Introduction to Statistical
Consulting:
Effective Communication
Slides prepared by David
Borgerding
Topics in Statistical Consulting
Introduction to Statistical Consulting.
The Ideal Statistical Consultant and the
Satisfied Client.
Non-Verbal Communicatio
Self Referencing and Join
How to use them
Self Referencing- If an attribute of a table refers
to another attribute of the same table
FacSuprSSN refers to the FacSSN - supervisors are
also faculty
VINCEs FacSuprSSN is 654321098
Faculty with FacSSN 65432
Nested Query or Sub Query
When to use Nested Query?
Group by and Aggregate Functions
Group by and Aggregate functions (e.g., max,
min, avg, count, sum) are closely related
Need Aggregate function?
Most likely you need group by
Except when the aggregat
Week 6 Lab
Using the Erwin Software construct a logical data model using the IE notation to fit the following
circumstances. Place all 5 solutions in a single Erwin file for convenience
Draw data models for the following situations. In each case show also
Nested Query or Sub Query
When to use Nested Query?
Nested Query or SubQuery
Subqueries must be enclosed within parentheses.
A subquery can have only one column in the
SELECT clause.
An ORDER BY cannot be used in a subquery.
Subqueries that return mor
Class 3 ITM 500 February 2nd 2017
SELECT: Fields/Attributes
FROM: Table Name
WHERE: Attribute (Operation =><) Value
Name like (N %) name that starts with N
ORDER BY (attributes)
% Any number of character
_ One character
Example 1: Name like (N%)
Any nam
Name:
Section:
MEMBER TABLE
TOURNAMENT TABLE
TOURENTRY TABLE
-1 List the average barbill by member type. Sequence the
output, highest to lowest average.
-2 List the total barbill for each team but only show the
teams where the total is less than $100
-3 L
-Lab Week 4
-Use the GolfClub Database.
-1 List the average barbill by member type. Sequence the output, highest to
lowest average.
select avg(Barbill) as "Average" , MemberType from Member
group by MemberType
order by avg(Barbill) desc;
-2 List the total
Pad 3
4:46 PM 1 56% -'
mathworks.com
Some functions, like sscanf and sprintf, precede conversion specifiers with the percent sign:
sprintf(' = 0', name, value)
Percent-Brace %cfw_ %)
The 95cfw_ and 96 symbols enclose a block of comments that extend bey
Faculty of Liberal Arts
and Professional Studies
Fall / Winter 2016-17
PPAS 3300 / SOCI 3030 / POLS 3300: Statistics for Social Science
Assignment 6
Solutions
For each of the following scenarios, perform the appropriate test and report the conclusion in n
Chapter 08
MULTIPLE CHOICE
1. Which of the following accurately describes a hypothesis test?
a. A descriptive technique that allows researchers to describe a sample
b. A descriptive technique that allows researchers to describe a population
c. An inferent
Introductory Statistics:
A Problem-Solving Approach
by Stephen Kokoska
Chapter 8: Confidence
Intervals Based on a Single
Sample
Copyright 2015 by W. H. Freeman and Company. All rights
reserved.
1
Introduction
A single value of a statistic
computed from a
Introductory Statistics:
A Problem-Solving Approach
by Stephen Kokoska
Chapter 6
Continuous Probability
Distributions
Copyright 2015 by W. H. Freeman and Company. All rights reserved.
1
Probability Density
Function
Copyright 2015 by W. H. Freeman and Comp
Introductory Statistics:
A Problem-Solving
Approach
by Stephen Kokoska
Chapter 3
Numerical Summary
Measures
Copyright 2015 by W. H.
Freeman and Company. All
rights reserved.
1
3.1 Measures of Central
Tendency
Tabular and graphical techniques
provide usefu
Introductory Statistics:
A Problem-Solving Approach
by Stephen Kokoska
Chapter 9: Hypothesis Tests
Based on a Single Sample
Copyright 2015 by W. H. Freeman and Company. All rights reserved.
1
Hypothesis
In statistics, a hypothesis is a
declaration, or cla
Introductory Statistics:
A Problem-Solving Approach
by Stephen Kokoska
Chapter 2
Tables and Graphs for
Summarizing Data
Copyright 2015 by W. H.
Freeman and Company. All
rights reserved.
1
Tables, Charts, Graphs
Used to organize and summarize data.
1.
2.
3
Introductory Statistics:
A Problem-Solving Approach
by Stephen Kokoska
Chapter 5
Random Variables and
Discrete
Probability Distributions
Copyright 2015 by W. H. Freeman and Company. All rights reserved.
1
5.1 Random Variable
A random variable is a functio
Introductory Statistics:
A Problem-Solving Approach
by Stephen Kokoska
Chapter 7
Sampling Distributions
Copyright 2015 by W. H. Freeman and Company. All rights reserved.
1
Parameter and Statistic
A parameter is a numerical
descriptive measure of a
populat
$- Sheeu- HILL-est Squares-MP _
.-._ _
ABRespomeIneome/Cap
A Whole Model I EBachGrad
D EffectSummaly A Leverage Plot
JRegressionPlot 40000 .
m _
3 35000
3 I I
E 30000
'1'
m
25000 _
-l
gamm
E 15000
a
E e ,e. e I
Problem 11.6
A.)
Model Summaryb
Std. Error
Adjusted
of the
Model
R
R Square R Square Estimate
1
.943a
.889
.878 4.59830
a. Predictors: (Constant), X
b. Dependent Variable: Y
ANOVAa
Sum of
Mean
Model
Squares df
Square
1
Regression
1695.473 1
1695.473
Resid
9.24
9.25
It seems the majority of observations in the selected data (cases 57-113) have an age between 50-57
B.
Correlations
Age Infection
Risk
Age
Infection Risk
Routine Culturing
Ratio
Routine X-Ray
Ratio
Number of Beds
Average Daily
Census
Number of
N
Assignment 5
Do problems 8.8, 9.24, 9.26.
8.8
a.
The response function does appear to provide a good fit. The plotted points have a fairly clear,
linear arrangement.
b.
Model Summaryb
Model
R
R Square
Adjusted R
Std. Error of
Square
the Estimate
a
1
.783