Popular in Economics

Economics

Gender and Economics

University of California Santa Barbara Shelly Lundberg

Banks and Policy Organisations

Greening the financial sector

European Investment Bank Matteo Ferrazzi

Economic History

Gravity with Gravitas: A Solution to the Border Puzzle

Boston College James E. Anderson

Economic Inequalities

Visions of Inequality

City University of New York Branko Milanovic

Economics

School Finance Equalization Increases Intergenerational Mobility

Yale University Barbara Biasi

Economics

Olivier Blanchard on U.S. Pandemic-Era Inflation

Peterson Institute for International Economics Olivier Blanchard

Nonparametric estimation of accelerated failure-time models with unobservable confounders and random censoring

View more insights in Social Sciences - Economics - Econometrics

View more figures from Toulouse School of Economics

View the page of Jean-pierre Florens

Read the Study

Jean-Pierre Florens has made influential contributions to a wide range of different topics in econometrics and statistics, including treatment effects, Bayesian inference, econometrics of stochastic processes, causality, frontier estimation, econometrics of game-theoretic model

Image courtesy of interviewee. November 27, 2023

Generating Workspace

Export as PDF

This video video appears to be a segment of a seminar or presentation discussing statistical modeling, data distribution, and issues related to treatment and estimation within various contexts, possibly including econometric applications. Key points include:

1. **Statistical Models**: The discussion revolves around statistical models that estimate data distributions and address problems involving treatment and its effectiveness.

2. **Data Processing**: Emphasis is placed on the necessity to accurately estimate processes governing data, which can involve complex equations and the identification of correct application methods.

3. **Exploratory Data Analysis**: There is mention of exploratory models used to analyze and predict outcomes, along with the challenges of ensuring that models are appropriately structured for the data.

4. **Application of Models**: The presentation raises concerns about the application of models to real-world situations, particularly in medical or economic contexts, emphasizing the importance of accurate protocols and appropriate interpretations of data.

5. **Risks and Estimation**: This video video also touches on the risks associated with data estimation, notably in relation to treatment effects, and the importance of deriving reliable estimates to avoid biased conclusions.

Overall, the text seems to convey a complex discussion about the mechanics of statistical modeling and practical challenges encountered in data analysis and application.

Language:

Export as PDF

Export as PDF
Here are some similar articles that discuss statistical modeling, data distribution, exploratory data analysis, and their applications in various fields:

1. **Understanding Statistical Models**:
- Article: *"An Overview of Statistical Models and Their Applications in Econometrics"*
- Summary: This article delves into various statistical models used in econometrics, discussing their methodology, significance, and practical applications in economic data analysis.

2. **Data Processing Techniques**:
- Article: *"Effective Data Processing Techniques for Accurate Statistical Analysis"*
- Summary: Focused on methods for processing data to enhance the accuracy of statistical models, this article highlights the importance of rigorous data preparation and methodological consistency.

3. **Exploratory Data Analysis**:
- Article: *"The Importance of Exploratory Data Analysis in Modern Statistics"*
- Summary: This piece emphasizes the role of exploratory data analysis (EDA) in understanding data distributions, detecting outliers, and guiding the modeling process in research.

4. **Challenges in Model Application**:
- Article: *"Bridging the Gap: Application of Statistical Models to Real-World Problems"*
- Summary: This article addresses the difficulties faced when applying theoretical models to practical scenarios, particularly in healthcare and economics, advocating for robust evaluation techniques.

5. **Estimation and Risks in Statistical Modeling**:
- Article: *"Assessing Risks in Statistical Estimation: A Guide for Practitioners"*
- Summary: This guide discusses various risks involved in statistical estimation, particularly concerning treatment effects and how to minimize bias in conclusions drawn from data analysis.

6. **Statistical Modeling in Health Economics**:
- Article: *"Statistical Modeling Approaches in Health Economics: Challenges and Solutions"*
- Summary: This article explores specific challenges faced by health economists in building and applying statistical models, including the evaluation of medical treatments and intervention effectiveness.

By reviewing these articles, you can gain a deeper understanding of the concepts introduced in your original video regarding statistical modeling, data distribution, and their practical implications.

X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

00:01.080 --> 00:02.080
Field off was

00:02.260 --> 00:05.880
to tu considères models statistical modele wa

00:06.430 --> 00:11.120
well object inf dle illat ce distribution of the data

00:11.650 --> 00:15.560
which stori estimate de General fins

00:15.820 --> 00:19.560
and id par of time tout

00:20.310 --> 00:23.120
ware exploratory able of modele with pla

00:23.120 --> 00:27.920
and out com is not génér de ranoblir independent le

00:27.920 --> 00:31.960
to the n ou présente sélection b

00:32.580 --> 00:35.520
en coding problème il par

00:36.820 --> 00:40.800
il portant un traitement ds Wells the traitement is not

00:42.010 --> 00:46.120
aside polli ums triment is

00:47.470 --> 00:52.360
isen ou ou a sont par in information à

00:52.360 --> 00:56.040
base debited ser

00:58.370 --> 01:03.080
été un been prouve inodore ouvers

01:03.670 --> 01:06.440
correct the w have

01:06.500 --> 01:10.400
to first est estime the data process sol

01:11.160 --> 01:13.680
équation which caractérise solution.

01:15.130 --> 01:19.120
Soyez voir que l'hôte General et moi

01:20.150 --> 01:24.960
Maurice à concentrer tout problèmes.

01:25.330 --> 01:28.440
Wells out com the result of of the

01:31.260 --> 01:35.960
par du of life du

01:36.340 --> 01:37.480
of of life

01:37.610 --> 01:41.760
after the starting of for for du

01:42.180 --> 01:44.200
of sur employment

01:48.220 --> 01:49.320
the second Fischer of

01:52.340 --> 01:54.520
duy exceptionnel.

01:54.540 --> 01:57.560
Tu observe compris du du for

01:57.620 --> 02:00.200
for people in the saint du ation art

02:02.710 --> 02:03.560
observe the fact

02:14.340 --> 02:17.320
effect in application.

02:18.500 --> 02:22.680
Oui, stop observation mais ends somer leur style living

02:23.260 --> 02:24.640
ofi à after time

02:31.380 --> 02:35.120
papers the on you you in côte

02:36.370 --> 02:40.000
ation model wf sing and we triment

02:40.020 --> 02:41.640
and w the triment dis not

02:43.740 --> 02:48.480
randit est difficile tout à plat in gold stard

02:49.690 --> 02:53.720
radom criel triel mode modèle for différentes be

02:53.720 --> 02:55.560
because ainsi qui

02:57.140 --> 03:00.960
il particule in in in the us of

03:02.420 --> 03:06.280
it in Olli

03:07.700 --> 03:10.800
inform à base

03:12.130 --> 03:16.840
deign i fit du trito b

03:21.110 --> 03:23.360
data a the is

03:26.270 --> 03:27.760
technical fort

03:32.150 --> 03:33.760
tout correct the b

03:39.020 --> 03:41.600
ici ou différentes co.

03:41.600 --> 03:43.120
Hauteurs indifférentes.

03:43.700 --> 03:47.160
Format stes sur général frame the

03:47.210 --> 03:50.320
after we have to où quelque considère différentes

03:50.320 --> 03:51.640
spécifications de force du

03:52.640 --> 03:56.000
accélérait l Cox modele non.

03:56.000 --> 04:00.440
Paramétrique de so work in several

04:01.700 --> 04:06.560
domain of parar du re models so w saw

04:07.580 --> 04:09.640
non andom ament

04:14.600 --> 04:16.120
y furent intéressé, bye bye bye.

04:16.120 --> 04:19.280
By infect the CRT

04:21.060 --> 04:25.040
the pochettes heurt de for

04:25.700 --> 04:27.320
it process be

04:27.320 --> 04:30.840
because it infect

04:35.820 --> 04:39.120
et en général, considère models

04:40.260 --> 04:43.040
coming risk model chu of the du

04:43.830 --> 04:45.760
mais baby baby différent.

04:46.300 --> 04:48.720
Oui, concerne tou

04:51.140 --> 04:52.440
de différentes possibilités.

04:53.580 --> 04:55.930
Oui, oui, oui, oui.

04:56.290 --> 04:59.010
Estimate ce data générative processus

05:00.180 --> 05:02.520
eu at bye bye.

05:02.940 --> 05:07.400
So aer Meier

05:07.400 --> 05:11.760
standard estimate after.

05:11.760 --> 05:16.230
Oui. Oui. Weekend show à ce param inter

05:16.690 --> 05:21.090
ri effect il solution of équation param

05:22.230 --> 05:27.210
by by so at

05:27.210 --> 05:28.650
almost problème of

05:28.830 --> 05:33.050
and degen of coding baby for

05:33.980 --> 05:37.970
malal the el of

05:38.750 --> 05:40.570
is estimated in

05:47.310 --> 05:51.050
ce problème est relativ easy, because not concern.

05:53.030 --> 05:56.170
Il fut à working w continu you street main en continuus

05:57.080 --> 06:01.760
instrumental able fonctionnel intégral

06:02.280 --> 06:06.840
équation one of the mathematical échoue of problème

06:07.380 --> 06:09.080
is to de is the so.

06:09.580 --> 06:12.240
Il pose problèmes et d'ailleurs

06:20.380 --> 06:24.120
of sous ce mathématique problème his sous transforme

06:25.100 --> 06:29.560
the propre of observation estimation

06:29.560 --> 06:34.080
of the distribution of the data in Toy of the object

06:35.130 --> 06:39.000
which the fonctionnel param des mode

06:40.220 --> 06:43.000
effect duly ce il

06:43.000 --> 06:46.080
post in z.

06:47.550 --> 06:49.530
Oui, oui, oui. Sommes coup de parti

06:49.590 --> 06:53.650
of estimation dans un procès équation

06:56.890 --> 07:01.730
a sa mathém in com right

07:01.760 --> 07:06.690
convergence distribution of initiale estimation

07:08.080 --> 07:12.210
part wa inter which fonction

07:13.320 --> 07:17.490
différents sont machines ding, machine landing, puis

07:18.830 --> 07:20.930
ou estimate ver c

07:21.980 --> 07:26.170
datae process elles tout predict s c par

07:29.030 --> 07:33.010
in in structurale économétrique sauf in in des fêtes

07:33.010 --> 07:37.530
where is wi randers siman ou un tout

07:38.390 --> 07:40.490
s with the co s ou Charlotte?

07:40.710 --> 07:44.770
Dre clear ce data process en in

07:45.790 --> 07:48.150
the traitement is différent

07:51.370 --> 07:55.550
to job, quakers z et we

07:56.510 --> 07:59.950
instinctif tri tout

08:02.180 --> 08:06.590
skim so une terrorise population tri

08:07.810 --> 08:10.430
the first to part à ask

08:12.860 --> 08:16.470
employeur réc bénéfice on affirme wi

08:17.970 --> 08:19.630
the bénéfices.

08:20.170 --> 08:21.470
Oh, no, no,

08:26.980 --> 08:27.270
puis

08:35.210 --> 08:35.430
bi.

08:35.740 --> 08:36.230
Because

08:46.470 --> 08:51.270
personel to blan group you create sélection b

08:52.890 --> 08:56.080
so ce traitement is

08:58.180 --> 09:01.000
il se communition of as of the décision

09:01.860 --> 09:03.160
the instrument is the as

09:03.900 --> 09:08.520
and z during duquel correct effect

09:18.910 --> 09:22.200
application wetar very

09:23.340 --> 09:27.410
to tow will the

09:28.430 --> 09:30.410
canere Paul in in Toulouse

09:30.710 --> 09:32.690
and so we toulou

09:36.280 --> 09:39.130
ment of because oui.

09:41.650 --> 09:46.150
In the of thenot be it

09:47.650 --> 09:49.950
en caractéristiques of the which.

09:56.210 --> 10:00.150
Il park

10:02.740 --> 10:07.110
star COVID par exemple, w have time to ply

10:09.820 --> 10:11.990
data arrive and traitement, arrive

10:13.010 --> 10:15.790
wi out correcte protocole for

10:17.690 --> 10:21.830
so w data en week

10:22.460 --> 10:23.870
data by by

10:24.690 --> 10:28.070
by the ce

10:37.290 --> 10:41.350
Que To

10:44.210 --> 10:48.030
the data ou a LUN

10:49.170 --> 10:53.800
est de estrie

10:55.990 --> 10:57.320
sive radiothérapie b

10:57.320 --> 11:01.680
before et tout t effectif

11:02.300 --> 11:06.680
the radiothérapie befor the différentes.

11:12.330 --> 11:14.870
Il se competing risque et laps

11:21.890 --> 11:25.230
d' Sting

11:26.620 --> 11:31.110
project braver

11:31.900 --> 11:36.830
with the Dynamic triment ner supplément

11:38.480 --> 11:40.670
cause fort for non rand.

11:40.970 --> 11:45.830
Si se ait GE detriment in in the

11:46.440 --> 11:47.470
elles sont ici.

11:48.010 --> 11:51.030
Si ses yeux jolies de peine, en particulier Fischer of the.

00:01 - Field off was to tu considères models statistical modele wa well object inf dle illat ce distribution of the data which stori estimate de General fins and id par of time tout ware exploratory able of modele with pla

00:23 - and out com is not génér de ranoblir independent le to the n ou présente sélection b en coding problème il par il portant un traitement ds Wells the traitement is not aside polli ums triment is isen ou ou a sont par in information à base debited ser été un been prouve inodore ouvers correct the w have to first est estime the data process sol

01:11 - équation which caractérise solution. Soyez voir que l'hôte General et moi Maurice à concentrer tout problèmes. Wells out com the result of of the par du of life du of of life after the starting of for for du of sur employment the second Fischer of duy exceptionnel.

01:54 - Tu observe compris du du for for people in the saint du ation art observe the fact effect in application. Oui, stop observation mais ends somer leur style living ofi à after time papers the on you you in côte ation model wf sing and we triment and w the triment dis not randit est difficile tout à plat in gold stard

02:49 - radom criel triel mode modèle for différentes be because ainsi qui il particule in in in the us of it in Olli inform à base deign i fit du trito b data a the is technical fort tout correct the b ici ou différentes co.

03:41 - Hauteurs indifférentes. Format stes sur général frame the after we have to où quelque considère différentes spécifications de force du accélérait l Cox modele non. Paramétrique de so work in several domain of parar du re models so w saw non andom ament y furent intéressé, bye bye bye. By infect the CRT

04:21 - the pochettes heurt de for it process be because it infect et en général, considère models coming risk model chu of the du mais baby baby différent. Oui, concerne tou de différentes possibilités. Oui, oui, oui, oui. Estimate ce data générative processus

05:00 - eu at bye bye. So aer Meier standard estimate after. Oui. Oui. Weekend show à ce param inter ri effect il solution of équation param by by so at almost problème of and degen of coding baby for malal the el of is estimated in

05:47 - ce problème est relativ easy, because not concern. Il fut à working w continu you street main en continuus instrumental able fonctionnel intégral équation one of the mathematical échoue of problème is to de is the so. Il pose problèmes et d'ailleurs of sous ce mathématique problème his sous transforme the propre of observation estimation of the distribution of the data in Toy of the object which the fonctionnel param des mode

06:40 - effect duly ce il post in z. Oui, oui, oui. Sommes coup de parti of estimation dans un procès équation a sa mathém in com right convergence distribution of initiale estimation part wa inter which fonction différents sont machines ding, machine landing, puis ou estimate ver c datae process elles tout predict s c par

07:29 - in in structurale économétrique sauf in in des fêtes where is wi randers siman ou un tout s with the co s ou Charlotte? Dre clear ce data process en in the traitement is différent to job, quakers z et we instinctif tri tout skim so une terrorise population tri the first to part à ask employeur réc bénéfice on affirme wi

08:17 - the bénéfices. no, no, puis bi. Because personel to blan group you create sélection b so ce traitement is il se communition of as of the décision the instrument is the as and z during duquel correct effect

09:18 - application wetar very to tow will the canere Paul in in Toulouse and so we toulou ment of because oui. In the of thenot be it en caractéristiques of the which. Il park star COVID par exemple, w have time to ply data arrive and traitement, arrive

10:13 - wi out correcte protocole for so w data en week data by by by the ce Que To the data ou a LUN est de estrie sive radiothérapie b before et tout t effectif the radiothérapie befor the différentes.

11:12 - Il se competing risque et laps d' Sting project braver with the Dynamic triment ner supplément cause fort for non rand. Si se ait GE detriment in in the elles sont ici. Si ses yeux jolies de peine, en particulier Fischer of the.

Faculti AI can make mistakes