Faridani

This user is a PhD student in the field of operations research at Berkeley. He usually contributes to the following general topics:

• Stochastic Processes
• Optimization Algorithms
• Python Programming
• Micro Electro Mechanical Systems

Kaveh Zamani's Funcrasing

“ Remember the past, live in the present and hope for the future. ”

— Today's Motto of the Day

This user has been on Wikipedia for 17 years, 9 months and 4 days.

This user is a member of WikiProject Systems.

Contributions

At this moment, He is contributing to the following articles:

• Poisson process
• Counting process
• Closure problem
• Modeling point processes in R
• Poisson regression::Implementations
• Poisson regression in R :: later merged with Poisson regression
• spatial Poisson regression
• Poisson hidden Markov model
• Semi-Markov process
• Continuous knapsack problem
• Voronoi diagram
• Biogas
• Markov chain
• Calculating demand forecast accuracy need to clean up the commercial ads occasionally

Social Network Analysis and Text Mining

• Jon Kleinberg
• Lesk algorithm
• Stemming

Production and Logistics Sciences:

• Material Requirements Planning
• Dynamic lot size model
• Economic order quantity
• Economic production quantity
• Reorder point
• Reorder quantity
• Stock-keeping unit

User Interfaces:

• Pie menu
• Bill Buxton
• Fitts's law

Entrepreneurship:

• Honest Tea

Other contributions:

• Judith Donath
• Institute for the Future
• Doe Memorial Library
• TEDxSF
• Stanford R. Ovshinsky

People:

• Andrew Rudd

User boxes

• Template:User OR

To Do

• The definition of Bordered Hessian is extremely confusing I suggest using the definition used in Luenberger's book "Linear and Nonlinear Programming" I am adding it to my todo list. Will correct it soon. --Max Allen G (talk)
• Multidimensional scaling
• Zero inventory model
• Principal components analysis
• Black–Scholes
• Modern portfolio theory
• IronPython
• Extreme Programming
• PyUnit
• Wald test
• Computational learning theory
• Slope One

To Learn

• Partially observable Markov decision process
• Reinforcement Learning
• good intro to POMDP

Stochastic Analysis

• SAS/INSIGHT
• generalized linear model
• Poisson regression
• Poisson regression in MATLAB
• Poisson regression in SAS
• Generalized linear model in R
• generalized linear model in MATLAB
• generalized linear model in SAS
• Generalized linear model in SPSS

Short Term TODO

Poisson Regression in SPSS

1. Sorting in SPSS
2. Poisson regression in SPSS
3. Annotated SPSS Output

2D Spatial Statistical Analysis

Generalized Linear Models: logistic regression, Poisson regression, etc.

Friends

• [User:Vantelimus]
  
• [User:Michael_Hardy]
  
• [User:Melcombe]
  
• [User:Mpdelbuono]

Useful Stuff

Use # for auto numbering, * for bullets use <code></code> for Code Texts. When you want the text and the link to be different use this [[Optimal_control_theory|control]]control

{{Reflist|3}} Multicol {{colbegin|3}} {{lorem}} {{lorem}} {{colend}}

{{colbegin|3}}
{{lorem}}
{{lorem}}
{{colend}}

<syntaxhighlight lang="python">
Time	Visits	
0.00	158
0.50	177
1.00	207
1.50	133
2.00	134
2.50	119
3.00	103
</syntaxhighlight>

For codes

Time	Visits	
0.00	158
0.50	177
1.00	207
1.50	133
2.00	134
2.50	119
3.00	103

This is the table I usually use:

α β γ δ ε ζ η θ ι κ λ μ ν ξ ο π ρ σ ς τ υ φ χ ψ ω Γ Δ Θ Λ Ξ Π Σ Φ Ψ Ω

∫ ∑ ∏ √ − ± ∞ ≈ ∝ = ≡ ≠ ≤ ≥ × · ÷ ∂ ′ ″ ∇ ‰ ° ∴ Ø ø ∈ ∉ ∩ ∪ ⊂ ⊃ ⊆ ⊇ ¬ ∧ ∨ ∃ ∀ ⇒ ⇔ → ↔ ↑ ℵ - – —

Poisson regression in R

The R function for fitting a generalized linear model is glm(). A Poisson regression is done when a counting process is being observed.

Syntax

glm( numData˜roadType+weekDay, family=poisson(link=log), data=roadData) fits a model $Y_{i}$ → Poisson($\mu _{i}$), where log($\mu _{i}$) = $X_{i}$. setting family=poisson.

The following code does the Poisson regression in R

X<-read.table("C:/poissonvisits.txt", header = TRUE)
names(X)
attach(X)
hourofday <- Time
foo <- split(Visits, hourofday)
foo <- sapply(foo, sum)
barplot(foo, space = 0, xlab = "hour of the day", ylab = "total count")

#Starting from here we do the GLM
w <- Time/24 * 2 * pi
out1 <- glm(Visits ~ I(sin(w)) + I(cos(w)), family = poisson)
summary(out1)

out2 <- update(out1, . ~ . + I(sin(2 * w)) + I(cos(2 * w)))
summary(out2)

out3 <- update(out2, . ~ . + I(sin(3 * w)) + I(cos(3 * w)))
summary(out3)

anova(out1, out2, out3, test = "Chisq")
plot(hourofday, Visits, xlab = "hour of the day")

curve(predict(out2, data.frame(w = x/24 * 2 * pi), type = "response"),add = TRUE)

where the file poissonvisits.txt is defined as

Time	Visits	
0.00	158
0.50	177
1.00	207
1.50	133
2.00	134
2.50	119
3.00	103

For more information about glm() in R type ?glm

See Also

ANOVA

References

• Generalized linear models in R [1]

• Generalized Linear Models in R, GillWard [2]

• Dobson, A. J. (1990) An Introduction to Generalized Linear Models. London: Chapman and Hall.

• Hastie, T. J. and Pregibon, D. (1992) Generalized linear models. Chapter 6 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.

• McCullagh P. and Nelder, J. A. (1989) Generalized Linear Models. London: Chapman and Hall.

• Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. New York: Springer.