c# pdf to image without ghostscript: Convert pdf into fillable form SDK application API .net html winforms sharepoint wbook21-part62

CHAPTER 5. LIMITED DEPENDENT VARIABLE MODELS

200

estimates are reported for both r = 4 and r = 40. Results for two values of r

were reported because the inferences are dierent from these two models. It

should be noted that there is a tendency of the consistent EM estimates of

dispersion to overstate the precision, producing generally larger t−statistics

for the EM versus Gibbs estimates.

Table 5.1: EM versus Gibbs estimates

Gibbs

SAR

SAR r = 4

SAR r = 40

SEM

SEM r = 4

r= 40

CONSTANT

2.587

3.758

4.976

2.227

3.925

2.710

t−value

2.912

2.150

2.408

3.115

1.936

2.168

INCOME

-0.128

-0.213

-0.259

-0.123

-0.214

-0.143

t−value

-2.137

-1.583

-2.047

-2.422

-1.636

-1.719

HOUSING

-0.029

-0.037

-0.053

-0.025

-0.048

-0.032

t−value

-1.617

-1.416

-1.983

-1.586

-1.439

-1.791



0.429

0.325

0.411

0.279

0.311

0.315

t−value

2.175

3.285

1.766

1.796

Another reasonwhy theEM andGibbs estimates may be dierent is that

McMillen's approach requires that a model for the non-constant variance be

specied. The specication used by McMillen was: v

=0:0007INCOME

0:0004HOUSING

. This is quite dierent from the approach taken in the

Bayesian model that relies on v

estimates from Gibbs sampling.

There are functions for carrying out Bayesian Probit and Tobit versions

of all spatialautoregressivemodels. SAR models areimplemented by sarp

and sart

g, SEM models by semp

gand semt

g, SAC models by sacp

and sact

Tobit models can involve either left or right truncation. That is censored

observations canbe y values that fall below a limitingvalue or censoring can

take place above a limit value. The functions sart

g, sact

gand semt

allow the user to specify the type of censoring and a limit value. This

defaults to the typical case of left censoring at zero. The documentation for

the function sart

gis:

PURPOSE: Gibbs sampling spatial autoregressive Tobit model

y = p*Wy + Xb + e, e is N(0,sige*V)

y is a censored vector (assumed to be at zero)

V = diag(v1,v2,...vn), r/vi = ID chi(r)/r, r = Gamma(m,k)

B = N(c,T), sige = gamma(nu,d0), p = diffuse prior

---------------------------------------------------

USAGE: results = sart_g(y,x,W,prior,ndraw,nomit,start)

where: y = dependent variable vector (nobs x 1)

Convert pdf into fillable form - C# PDF Form Data fill-in Library: auto fill-in PDF form data in C#.net, ASP.NET, MVC, WinForms, WPF

Online C# Tutorial to Automatically Fill in Field Data to PDF

converting pdf to fillable form; best pdf form filler

Convert pdf into fillable form - VB.NET PDF Form Data fill-in library: auto fill-in PDF form data in vb.net, ASP.NET, MVC, WinForms, WPF

VB.NET PDF Form Data fill-in library: auto fill-in PDF form data in vb.net, ASP.NET, MVC, WinForms, WPF

pdf fillable forms; add fillable fields to pdf online

CHAPTER 5. LIMITED DEPENDENT VARIABLE MODELS

201

x = independent variables matrix (nobs x nvar)

W = 1st order contiguity matrix (standardized, row-sums = 1)

ndraw = # of draws

nomit = # of initial draws omitted for burn-in

prior = a structure for: B = N(c,T), sige = gamma(nu,d0)

prior.beta, prior means for beta,

c above (default 0)

prior.bcov, prior beta covariance , T above (default 1e+12)

prior.rval, r prior hyperparameter, default=4

prior.m,

informative Gamma(m,k) prior on r

prior.k,

(default: not used)

prior.nu,

a prior parameter for sige

prior.d0,

(default: diffuse prior for sige)

prior.trunc = 'left' or 'right' censoring (default = left)

prior.limit = value for censoring (default = 0)

start = (optional) structure containing starting values:

defaults: beta=1,sige=1,rho=0.5, V= ones(n,1)

start.b

= beta starting values (nvar x 1)

start.p

= rho starting value

(scalar)

start.sig = sige starting value (scalar)

start.V

= V starting values (n x 1)

---------------------------------------------------

NOTE: 1st column of x-matrix must contain iota vector (constant term)

---------------------------------------------------

RETURNS: a structure:

results.meth = 'sart_g'

results.bdraw = bhat draws (ndraw-nomit x nvar)

results.sdraw = sige draws (ndraw-nomit x 1)

results.vmean = mean of vi draws (1 x nobs)

results.rdraw = sige draws (ndraw-nomit x 1)

results.pdraw = p draws

(ndraw-nomit x 1)

results.ymean = mean of y draws (1 x nobs)

results.pmean = b prior means, prior.beta from input

results.pstd = b prior std deviations sqrt(diag(T))

results.r

= value of hyperparameter r (if input)

results.nobs = # of observations

results.nvar = # of variables in x-matrix

results.nobsc = # of censored y-values

results.ndraw = # of draws

results.nomit = # of initial draws omitted

results.y

= actual observations (nobs x 1)

results.yhat = predicted values

results.nu

= nu prior parameter

results.d0

= d0 prior parameter

results.time = time taken for sampling

results.accept= acceptance rate

results.rmax = 1/max eigenvalue of W (or rmax if input)

results.rmin = 1/min eigenvalue of W (or rmin if input)

C# Create PDF from OpenOffice to convert odt, odp files to PDF in

public override void ConvertToDocument(DocumentType targetType, Stream stream). Description: Convert to PDF/TIFF and save it into stream. Parameters:

convert word doc to fillable pdf form; change pdf to fillable form

C# Create PDF from Excel Library to convert xlsx, xls to PDF in C#

Turn all Excel spreadsheet into high quality PDF without Create searchable and scanned PDF files from Excel. Convert to PDF with embedded fonts or without

pdf signature field; convert pdf to pdf form fillable

CHAPTER 5. LIMITED DEPENDENT VARIABLE MODELS

202

To illustrate the case of Tobit model estimation, example 5.2 shows an

example where we generate an SAR model based on the Anselin neighbor-

hood crime spatial contiguity matrix. We censor observations that are less

than zero. Estimates based on the uncensored data using the function sar

are compared to those from using sar

gand sart

gon the censored data.

We would expect that ignoring censoring should produce poor estimates

from application of sar

gon the censored data whereas use of sart

gwould

produce better estimates that are closer to those from application of sar

to the uncensored data.

Avector y is generated based on the neighborhood crime independent

variables standardized. This standardization produces a relatively even

number of negative and positive values for y. Negative values are then

censored.

% ----- Example 5.2 SAR Tobit Model

load anselin.dat;

xt = [anselin(:,2:3)]; n = length(xt);

% center and scale the data so our y-values

% are evenly distributed around zero, the censoring point

x = [ones(n,1) studentize(xt)];

[n k] = size(x);

load wmat.dat;

W = wmat;

sige = 5.0;

evec = randn(n,1)*sqrt(sige);

rho = 0.75;

beta = ones(k,1);

B = eye(n) - rho*W;

BI = inv(B);

y = BI*x*beta + BI*evec;

yc = y;

% now censor neighborhoods with crime < 1

for i=1:n

if y(i,1) < 1

yc(i,1) = 1;

end;

Vnames = strvcat('crime','constant','income','hvalue');

ndraw = 600; nomit = 100;

prior.rval = 30;

res1 = sar(y,x,W);

res2 = sar_g(yc,x,W,ndraw,nomit,prior);

prior.limit = 1;

prior.trunc = 'left';

res3 = sart_g(yc,x,W,ndraw,nomit,prior);

prt(res1,Vnames);

prt(res2,Vnames);

prt(res3,Vnames);

C# Create PDF from PowerPoint Library to convert pptx, ppt to PDF

for turning all PowerPoint presentation into high quality PDF Convert to PDF with embedded fonts or without Convert multiple pages PowerPoint to fillable and

convert html form to pdf fillable form; create fillable pdf form

C# Create PDF from Word Library to convert docx, doc to PDF in C#.

NET control able to turn all Word text and image content into high quality PDF without losing formatting. Convert multiple pages Word to fillable and editable

create a fillable pdf form from a pdf; convert pdf fill form

CHAPTER 5. LIMITED DEPENDENT VARIABLE MODELS

203

The printed results for an SAR model based on the uncensored data as

well as a set of estimates that ignore the sample censoring and the Tobit

version of the SAR model are presented below.

Spatial autoregressive Model Estimates

Dependent Variable =

crime

R-squared

0.7344

Rbar-squared

0.7229

sigma^2

5.6564

Nobs, Nvars

49,

log-likelihood =

-99.041858

# of iterations =

min and max rho =

-1.5362,

1.0000

***************************************************************

Variable

Coefficient

t-statistic

t-probability

constant

0.617226

1.328646

0.190519

income

1.064967

2.271643

0.027831

hvalue

0.897449

2.254232

0.028988

rho

0.724180

5.068285

0.000007

Gibbs sampling spatial autoregressive model

Dependent Variable =

crime

R-squared

0.7013

sigma^2

3.4570

r-value

Nobs, Nvars

49,

ndraws,nomit

600,

100

acceptance rate =

0.9662

time in secs

14.4129

min and max rho =

-1.5362,

1.0000

***************************************************************

Variable

Prior Mean

Std Deviation

constant

0.000000

1000000.000000

income

0.000000

1000000.000000

hvalue

0.000000

1000000.000000

***************************************************************

Posterior Estimates

Variable

Coefficient

t-statistic

t-probability

constant

1.412658

3.037593

0.003922

income

1.253225

3.479454

0.001111

hvalue

0.364549

1.267454

0.211372

rho

0.599010

6.097006

0.000000

Gibbs sampling spatial autoregressive Tobit model

Dependent Variable =

crime

R-squared

0.6977

sigma^2

6.9121

r-value

Nobs, Nvars

49,

VB.NET Create PDF from PowerPoint Library to convert pptx, ppt to

Turn all PowerPoint presentation into high quality PDF Convert to PDF with embedded fonts or without Convert multiple pages PowerPoint to fillable and editable

convert pdf file to fillable form; asp.net fill pdf form

VB.NET Create PDF from Word Library to convert docx, doc to PDF in

all Word text and image content into high quality PDF Create PDF files from both DOC and DOCX formats. Convert multiple pages Word to fillable and editable PDF

convert word form to pdf fillable form; create pdf fillable form

CHAPTER 5. LIMITED DEPENDENT VARIABLE MODELS

204

# censored values =

ndraws,nomit

600,

100

acceptance rate

0.9120

time in secs

19.7692

min and max rho

-1.5362,

1.0000

***************************************************************

Variable

Prior Mean

Std Deviation

constant

0.000000

1000000.000000

income

0.000000

1000000.000000

hvalue

0.000000

1000000.000000

***************************************************************

Posterior Estimates

Variable

Coefficient

t-statistic

t-probability

constant

0.936753

1.847863

0.071058

income

1.479955

2.844135

0.006624

hvalue

0.544580

1.320079

0.193340

rho

0.629394

6.446799

0.000000

Akey to understanding how the Gibbs sampler works on these problems

is the generation of predicted values for the censored observations. These

values may also be useful for purposes of inference regarding the censored

observations. The tobit spatial autoregressive functions return a structure

variable eld `results.ymean'that represents the mean of the sampled values

for the censored observations as well as the actual values of the uncensored

observations. Figure5.2 shows aplot of this data vector against the actual y

variables. Ordinarily, we wouldn't know the values of the censored y values,

but in this case because we generated the data set and then censored the

observations we have this information.

5.5 An applied example

The Harrison and Rubinfeld Boston data set used in Chapter3 to illustrate

Gibbs sampling Bayesian spatial autoregressive models contains censored

values. Median house values greater than $50,000 were set equal to 50,000

for 16 of the 506 sample observations (see Gilley and Pace, 1995). This

provides an opportunity to see if using tobit estimation to take the sample

truncation into account produces dierent parameter estimates.

Example 5.3 reads the Boston data set and sorts by median housing

values. Note that we must also sort the explanatory variables using the

index vector `yind'returned by the sort for the y values as wellas the latitude

and longitude vectors. After carrying out Gibbs sampling estimationfor the

SAR, SEM and SAC models, we add to the prior structure variable a eld

for right-truncationand we supply the limit value which is the log of 50,000

VB.NET Create PDF from Excel Library to convert xlsx, xls to PDF

Turn all Excel spreadsheet into high quality PDF without Create searchable and scanned PDF files from Excel in VB Convert to PDF with embedded fonts or without

create fillable form from pdf; convert word to pdf fillable form

VB.NET Create PDF from OpenOffice to convert odt, odp files to PDF

Convert OpenOffice Spreadsheet data to PDF. Export PDF document from OpenOffice Presentation. Turn ODT, ODS, ODP forms into fillable PDF formats.

convert an existing form into a fillable pdf; convert word document to fillable pdf form

CHAPTER 5. LIMITED DEPENDENT VARIABLE MODELS

205

-10

-5

Observations

Crime

Actual

Mean of Gibbs samples

Figure 5.2: Actual vs. simulated censored y-values

standardized. By virtue of our sorting the y vector, this transformed limit

value must equal the last 16 observations, so we use the last to dene the

limit value.

% ----- Example 5.3 Right-censored Tobit for the Boston data

load boston.raw; % Harrison-Rubinfeld data

load latitude.data; load longitude.data;

[n k] = size(boston);y = boston(:,k);

% median house values

% sort by median house values

[ys yind] = sort(y); xs = boston(yind,1:k-1);

lats = latitude(yin,1); lons = longitude(yin,1);

[W1 W W3] = xy2cont(lats,lons); % create W-matrix

vnames = strvcat('hprice','constant','crime','zoning','industry', ...

'charlesr','noxsq','rooms2','houseage','distance','access','taxrate', ...

'pupil/teacher','blackpop','lowclass');

y = studentize(log(ys)); x = [ones(n,1) studentize(xs)];

% define censoring limit

limit = y(506,1); % median values >=50,000 are censored to 50

C# Create PDF Library SDK to convert PDF from other file formats

Create fillable PDF document with fields. with 2 blank pages PDFDocument doc = PDFDocument.Create(2); // Save the new created PDF document into file doc

create a pdf form to fill out and save; create a fillable pdf form

VB.NET Create PDF Library SDK to convert PDF from other file

Best VB.NET component to convert Microsoft Office Word cake, as it can be easily integrated into many MS Visual Studio .NET applications to create PDF with just

convert pdf fillable form to html; create a fillable pdf form online

CHAPTER 5. LIMITED DEPENDENT VARIABLE MODELS

206

ndraw = 1100; nomit = 100;

prior.rval = 4;

prior.rmin = 0; prior.rmax = 1;

prior.lmin = 0; prior.lmax = 1;

% ignore censoring

res1 = sar_g(y,x,W,ndraw,nomit,prior);

prt(res1,vnames);

res2 = sem_g(y,x,W,ndraw,nomit,prior);

prt(res2,vnames);

res3 = sac_g(y,x,W,W,ndraw,nomit,prior);

prt(res3,vnames);

% use Tobit for censoring

prior.trunc = 'right';

prior.limit = limit;

res4 = sart_g(y,x,W,ndraw,nomit,prior);

prt(res4,vnames);

res5 = semt_g(y,x,W,ndraw,nomit,prior);

prt(res5,vnames);

res6 = sact_g(y,x,W,W,ndraw,nomit,prior);

prt(res6,vnames);

Intuitively, we might not expect a large dierence in the parameter

estimates for this case where only 16 of the 506 sample observations are

censored. The results are presented below, where the information that is

typically printed regarding prior means and standard deviations has been

eliminated because we used a diuse prior. The order of the results has been

ordered to present both SAR and the tobit SAR, then the SEM and tobit

SEM and nally SAC and tobit SAC estimates.

Gibbs sampling spatial autoregressive model

Dependent Variable =

hprice

R-squared

0.8243

sigma^2

0.1921

r-value

Nobs, Nvars

506,

ndraws,nomit

1100,

100

acceptance rate =

0.8662

time in secs

= 129.8548

min and max rho =

0.0000,

1.0000

***************************************************************

Posterior Estimates

Variable

Coefficient

t-statistic

t-probability

constant

-0.025169

-1.126015

0.260708

crime

-0.152583

-3.509177

0.000491

zoning

0.050736

1.304237

0.192763

industry

0.045046

0.915162

0.360555

charlesr

0.020157

0.787959

0.431100

CHAPTER 5. LIMITED DEPENDENT VARIABLE MODELS

207

noxsq

-0.089610

-1.803644

0.071899

rooms2

0.267168

5.731838

0.000000

houseage

-0.036438

-0.878105

0.380315

distance

-0.178140

-5.673916

0.000000

access

0.188203

2.713291

0.006895

taxrate

-0.212748

-5.525728

0.000000

pupil/teacher

-0.117601

-3.980184

0.000079

blackpop

0.107424

3.873596

0.000122

lowclass

-0.313225

-7.068474

0.000000

rho

0.314435

3.212364

0.001403

Gibbs sampling spatial autoregressive Tobit model

Dependent Variable =

hprice

R-squared

0.8225

sigma^2

0.1595

r-value

Nobs, Nvars

506,

# censored values =

ndraws,nomit

1100,

100

acceptance rate

0.9206

time in secs

= 158.1523

min and max rho

0.0000,

1.0000

***************************************************************

Posterior Estimates

Variable

Coefficient

t-statistic

t-probability

constant

-0.034993

-1.763936

0.078363

crime

-0.159346

-3.631451

0.000311

zoning

0.051875

1.399918

0.162168

industry

0.025951

0.561893

0.574445

charlesr

0.010791

0.446021

0.655778

noxsq

-0.084991

-1.800372

0.072414

rooms2

0.240183

5.251582

0.000000

houseage

-0.048693

-1.265013

0.206465

distance

-0.176466

-5.657902

0.000000

access

0.193611

3.125944

0.001877

taxrate

-0.219662

-6.201483

0.000000

pupil/teacher

-0.115413

-4.128876

0.000043

blackpop

0.107766

4.064385

0.000056

lowclass

-0.301859

-7.543143

0.000000

rho

0.296382

2.937506

0.003464

Gibbs sampling spatial error model

Dependent Variable =

hprice

R-squared

0.7304

sigma^2

0.1445

r-value

Nobs, Nvars

506,

ndraws,nomit

1100,

100

acceptance rate

0.4870

CHAPTER 5. LIMITED DEPENDENT VARIABLE MODELS

208

time in secs

= 114.7631

min and max lambda =

0.0000,

1.0000

***************************************************************

Posterior Estimates

Variable

Coefficient

t-statistic

t-probability

constant

-0.039799

-0.420595

0.674234

crime

-0.165742

-4.010247

0.000070

zoning

0.049197

1.177168

0.239698

industry

-0.005192

-0.087059

0.930660

charlesr

-0.015074

-0.579339

0.562625

noxsq

-0.147566

-1.925225

0.054777

rooms2

0.338988

8.416259

0.000000

houseage

-0.127886

-2.885708

0.004077

distance

-0.175342

-2.494614

0.012936

access

0.276185

3.014877

0.002704

taxrate

-0.237284

-4.804194

0.000002

pupil/teacher

-0.084627

-2.720154

0.006756

blackpop

0.144584

4.584164

0.000006

lowclass

-0.243651

-5.941416

0.000000

lambda

0.786425

19.084355

0.000000

Gibbs sampling spatial error Tobit model

Dependent Variable =

hprice

R-squared

0.7313

sigma^2

0.1493

r-value

Nobs, Nvars

506,

# censored values =

ndraws,nomit

1100,

100

acceptance rate

0.4808

time in secs

= 142.1482

min and max lambda =

0.0000,

1.0000

***************************************************************

Posterior Estimates

Variable

Coefficient

t-statistic

t-probability

constant

-0.039910

-0.544675

0.586224

crime

-0.155688

-4.035574

0.000063

zoning

0.049964

1.139609

0.255004

industry

-0.013470

-0.220422

0.825634

charlesr

-0.019834

-0.649249

0.516481

noxsq

-0.108960

-1.401207

0.161783

rooms2

0.277678

5.450260

0.000000

houseage

-0.116271

-2.463411

0.014103

distance

-0.161768

-2.319974

0.020751

access

0.255551

2.665245

0.007946

taxrate

-0.241885

-4.658895

0.000004

pupil/teacher

-0.088207

-2.694077

0.007300

blackpop

0.134053

4.262707

0.000024

lowclass

-0.259032

-5.544375

0.000000

CHAPTER 5. LIMITED DEPENDENT VARIABLE MODELS

209

lambda

0.728140

12.943930

0.000000

Gibbs sampling general spatial model

Dependent Variable =

hprice

R-squared

0.8601

sigma^2

0.1468

r-value

Nobs, Nvars

506,

ndraws,nomit

1100,

100

accept rho rate

0.9857

accept lam rate

0.5040

time in secs

= 214.1628

min and max rho

0.0000,

1.0000

min and max lambda =

0.0000,

1.0000

***************************************************************

Posterior Estimates

Variable

Coefficient

t-statistic

t-probability

constant

-0.026721

-0.556941

0.577821

crime

-0.167938

-4.414590

0.000012

zoning

0.047543

1.179076

0.238938

industry

0.016986

0.299507

0.764680

charlesr

0.009328

0.343660

0.731249

noxsq

-0.128901

-1.944391

0.052418

rooms2

0.330110

7.369387

0.000000

houseage

-0.099118

-2.108214

0.035518

distance

-0.191117

-3.638851

0.000303

access

0.235348

2.793913

0.005411

taxrate

-0.232871

-4.977616

0.000001

pupil/teacher

-0.108696

-3.825547

0.000147

blackpop

0.133964

4.398670

0.000013

lowclass

-0.297188

-7.347890

0.000000

rho

0.717992

13.037376

0.000000

lambda

0.083388

1.815698

0.070025

Gibbs sampling general spatial Tobit model

Dependent Variable =

hprice

R-squared

0.8602

sigma^2

0.1333

r-value

Nobs, Nvars

506,

# censored values =

ndraws,nomit

1100,

100

accept rho rate

0.9839

accept lam rate

0.7113

time in secs

= 249.0965

min and max rho

0.0000,

1.0000

min and max lambda =

0.0000,

1.0000

***************************************************************

Posterior Estimates

Documents you may be interested

c# pdf to image without ghostscript : Convert pdf into fillable form SDK application API .net html winforms sharepoint wbook21-part62