Last modified by Maria Valaste on 2024/04/16 14:49
Show last authors
1 == [[Home >>url:https://wiki.helsinki.fi/xwiki/bin/view/BNU/Events/Summer%20School%20on%20Survey%20Statistics%202021/]]| [[Registration >>url:https://wiki.helsinki.fi/xwiki/bin/view/BNU/Events/Summer%20School%20on%20Survey%20Statistics%202021/Summer%20School%20on%20Survey%20Statistics%202021%20Registration/]]| [[Important Dates>>url:https://wiki.helsinki.fi/xwiki/bin/view/BNU/Events/Summer%20School%20on%20Survey%20Statistics%202021/Summer%20School%20on%20Survey%20Statistics%202021%20Important%20Dates/]] | [[Scientific Programme>>url:https://wiki.helsinki.fi/xwiki/bin/view/BNU/Events/Summer%20School%20on%20Survey%20Statistics%202021/Summer%20School%20on%20Survey%20Statistics%202021%20Scientific%20Programme/]] | [[Speakers >>url:https://wiki.helsinki.fi/xwiki/bin/view/BNU/Events/Summer%20School%20on%20Survey%20Statistics%202021/Summer%20School%20on%20Survey%20Statistics%202021%20Speakers/]]| [[Instructions for Speakers>>url:https://wiki.helsinki.fi/xwiki/bin/view/BNU/Events/Summer%20School%20on%20Survey%20Statistics%202021/Summer%20School%20on%20Survey%20Statistics%202021%20Instructions%20for%20Speakers/]] | [[Sponsors >>url:https://wiki.helsinki.fi/xwiki/bin/view/BNU/Events/Summer%20School%20on%20Survey%20Statistics%202021/Summer%20School%20on%20Survey%20Statistics%202021%20Committees/Summer%20School%20on%20Survey%20Statistics%202021%20Sponsors/]]| [[Committees >>url:https://wiki.helsinki.fi/xwiki/bin/view/BNU/Events/Summer%20School%20on%20Survey%20Statistics%202021/Summer%20School%20on%20Survey%20Statistics%202021%20Committees/]]| [[Contacts>>url:https://wiki.helsinki.fi/xwiki/bin/view/BNU/Events/Summer%20School%20on%20Survey%20Statistics%202021/Summer%20School%20on%20Survey%20Statistics%202021%20Contacts/]] ==
2
3 = Speakers =
4
5
6 == Keynote speakers ==
7
8 (% class="relative-table wrapped" style="width:100.0%" %)
9 |=(((
10 Speaker
11 )))|=(((
12 Title
13 )))|=(((
14 Abstract
15 )))
16 |(((
17 (% class="content-wrapper" %)
18 (((
19 [[image:attach:Photo_ShuYang2.jpg||height="120" width="100"]]P(% style="letter-spacing:0.0px" %)h.D., Associate professor of statistics, (%%) (% style="letter-spacing:0.0px" %)**Shu Yang**(%%) (% style="letter-spacing:0.0px" %)(North Carolina State University, USA)(%%)Shu Yang is an associate professor of Statistics at NC State University. She received her Ph.D. in Applied Mathematics and Statistics from Iowa State University and postdoctoral training at Harvard T.H. Chan School of Public Health. Her primary research interest is causal inference and data integration, particularly with applications to comparative effectiveness research in health studies. She also works extensively on methods for missing data, spatial statistics, and sampling statistics. She has been Principal Investigator for several U.S. National Science Foundation and National Institute of Health research projects.
20 )))
21 )))|(((
22 Data integration: a new paradigm  for survey statistics
23 )))|(((
24 Finite population inference is a central goal in survey sampling. Probability sampling is the gold standard statistical approach to finite population inference. Challenges arise due to high costs and increasing non-response rates. Data integration provides a timely solution by leveraging multiple data sources to provide more robust and efficient inference than using any single data source alone. The technique for data integration varies depending on the types of samples and available information to be combined. This talk provides a systematic review of data integration techniques for combining probability and non-probability samples and for combining probability and big data samples. A wide range of integration methods will be covered such as calibration weighting, inverse probability weighting, mass imputation, and doubly robust methods. Finally, I will highlight important questions for future research.
25 )))
26 |(((
27 (% class="content-wrapper" %)
28 (((
29 Prof. Dr. ** Piet Daas** (Eindhoven University of Technology & Statistics Netherlands) [[image:attach:image2021-5-28_16-7-8.png||height="108" width="66"]]
30
31 Prof. Dr. Piet Daas is a senior-methodologist at Statistics Netherlands where he leads the research on Big Data. He is a professor by  special appointment on ‘Big Data in Official Statistics’ at the Eindhoven University of Technology where his work focuses on the development of Big Data methodology. Piet teaches Big Data (on-line) at Statistics Netherlands, the University of Utrecht, University of Maryland, and the University of Mannheim.
32 )))
33 )))|(((
34 Identifying different types of companies via their website text
35 )))|(((
36 The internet and especially web pages are a very interesting source of data. It has very interesting potential applications such as providing novel insights on the activities of companies, to inform policy makers and also for official statistics, especially when performed at large scale. However, extracting relevant and reliable information from big data sources in a reproducible way is not an easy task. In this presentation results of Machine Learning based classifications of web sites texts are discussed in relation to the identification of innovative, platform economy and AI companies.
37 )))
38 |(% colspan="1" %)(% colspan="1" %)
39 (((
40 [[image:attach:photo_Szymkowiak2.jpg||height="142" width="100"]]
41
42 (% class="content-wrapper" style="text-align: left;" %)
43 (((
44 (% style="letter-spacing:0.0px" %)A(% style="color:#000000; letter-spacing:0.0px" %)ssociate Professor,  Deputy Director (% style="letter-spacing:0.0px" %)**Marcin Szymkowiak, (**(% style="color:#000000; letter-spacing:0.0px" %)Department of Statistics, Poznan University of Economics and Business;  Statistical Office in Poznan)(% style="color:#000000" %)Associate Professor at Department of Statistics, Poznan University of Economics and Business and Deputy Director at Statistical Office in Poznan, with many years of experience in the field of statistical data analysis. He specializes in small area estimation, methods of dealing with nonresponse (imputation and calibration), survey sampling, statistical methods of data integration (probabilistic record linking, statistical matching), and multivariate data analysis. He has participated in many domestic and international projects in cooperation with the Central Statistical Office in Poland, the World Bank and Eurostat (for instance Eurarea, ESSnet on Small Area Estimation, MEETS, ESSnet on Data Integration, VIP Admin, Memobust, Improvement of EU censuses quality from 2021, Foreigners in the Polish regional labour market, Extension of the Labour Force Survey indicators of labour market and education).
45 )))
46 )))|(% colspan="1" %)(% colspan="1" %)
47 (((
48 (% style="color:#000000" %)Small area estimation in official statistics - past, present and future directions of applications
49 )))|(% colspan="1" %)(% colspan="1" %)
50 (((
51 Small area estimation methodology (SAE) has been developed to produce reliable estimates of different characteristics of interest, such as means, counts, quantiles or ratios for domains for which only small samples are available. From that point of view SAE has become a topic of great importance due to the growing demand for reliable small area statistics. SAE methodology is used by different national statistical institutes in different areas, in particular to estimate quantities that are related to the labour market, agriculture or business statistics. It is also useful for mapping poverty. For instance, the World Bank has used SAE methodology to prepare poverty maps for tens countries all over the world. The main purpose of this presentation is to provide a review of the main applications in SAE methods, mainly in official statistics. Presentation will be based on earlier and present applications which serve as a necessary background for the new directions of the SAE development, including using big data sources as an example.
52 )))
53
54 == Invited lecturers in Russian ==
55
56 (% class="relative-table wrapped" style="width:100.0%" %)
57 |=(((
58 Speaker
59 )))|=(((
60 Title
61 )))|=(((
62 Abstract
63 )))
64 |(((
65 **Tetiana Ianevych **((% style="color:#000000" %)Taras Shevchenko National University of Kyiv, Ukraine)
66 )))|(((
67 (% style="color:#000000" %)Sample Surveys: Main Estimation Methods
68 )))|(((
69 (% class="content-wrapper" %)
70 (((
71
72
73 {{view-file att--filename="Ianevych_abstract.pdf" height="150"/}}
74 )))
75 )))
76 |(((
77 **Iryna Rozora **((% style="color:#000000" %)Taras Shevchenko National University of Kyiv, Ukraine)
78 )))|(((
79 (% style="color:#000000" %)Calibration Estimation for Nonresponse Bias Reduction
80 )))|(((
81 (% class="content-wrapper" %)
82 (((
83
84
85 {{view-file att--filename="Latex template for lectures_Rozora.pdf" height="150"/}}
86 )))
87 )))
88 |(((
89 **Tetiana Manzhos** ((% style="color:#000000" %)Kyiv National Economic University, Ukraine)
90 )))|(((
91 (% style="color:#000000" %)Big Data and Sample Surveys: Problems of Use
92 )))|(((
93 (% class="content-wrapper" %)
94 (((
95
96
97 {{view-file att--filename="T.Manzhos.doc" height="150"/}}
98 )))
99 )))
100 |(((
101 **Olga Vasylyk** ((% style="color:#000000" %)National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Ukraine)
102 )))|(((
103 (% style="color:#000000" %)Estimation for Domains and Small Areas
104 )))|(((
105 (% class="content-wrapper" %)
106 (((
107
108
109 {{view-file att--filename="Vasylyk_Abstract_BNUSS2021.pdf" height="150"/}}
110 )))
111 )))
112
113 == Invited speakers ==
114
115 (% class="relative-table wrapped" style="width:100.0%" %)
116 |=(((
117 Speaker
118 )))|=(((
119 Title
120 )))|=(((
121 Abstract
122 )))
123 |(((
124 (% style="color:#000000" %)**Kaja Sõstra** (Estonia)
125 )))|(((
126 SAE Methods for Developing the Digital Economy and Society Index (DESI) at local level
127 )))|(((
128 (% class="content-wrapper" %)
129 (((
130
131
132 {{view-file att--filename="Abstract_Sostra.pdf" height="150"/}}
133 )))
134 )))
135 |(% colspan="1" %)(% colspan="1" %)
136 (((
137 (% style="color:#000000" %)**Tomas Rudys** (Lithuania)
138 )))|(% colspan="1" %)(% colspan="1" %)
139 (((
140 The use of alternative data sources at Statistics Lithuania
141 )))|(% colspan="1" %)(% colspan="1" %)
142 (((
143 (% class="content-wrapper" %)
144 (((
145
146
147 {{view-file att--filename="Rudys_abstract_2021.pdf" height="150"/}}
148 )))
149 )))
150 |(% colspan="1" %)(% colspan="1" %)
151 (((
152 (% style="color:#000000" %)**Signe Bāliņa** (Latvia)
153 )))|(% colspan="1" %)(% colspan="1" %)
154 (((
155 What is behind statistics?
156 )))|(% colspan="1" %)(% colspan="1" %)
157 (((
158
159 )))
160 |(% colspan="1" %)(% colspan="1" %)
161 (((
162 (% style="color:#000000" %)**Sylwia Filas-Przybył** (Statistical Office in Poznań, Adam Mickiewicz University in Poznań) and **Tomasz Klimanek** (Statistical Office in Poznań, Poznań University of Economics and Business)
163 )))|(% colspan="1" %)(% colspan="1" %)
164 (((
165 (% style="color:#000000" %)Income stratification of the urban population in Poland
166 )))|(% colspan="1" %)(% colspan="1" %)
167 (((
168 (% class="content-wrapper" %)
169 (((
170
171
172 {{view-file att--filename="filas_klimanek.pdf" height="150"/}}
173 )))
174 )))
175 |(% colspan="1" %)(% colspan="1" %)
176 (((
177 **Krista Lagus **(University of Helsinki, Finland)
178 )))|(% colspan="1" %)(% colspan="1" %)
179 (((
180 Open-ended questions in surveys: Exploring the possibilities of NLP and data science
181 )))|(% colspan="1" %)(% colspan="1" %)
182 (((
183
184 )))
185 |(% colspan="1" %)(% colspan="1" %)
186 (((
187 (% style="color:#222222" %)**Blaise Ngendangenzwa** and **Joel Tolsheden**  (Statistics Sweden)
188 )))|(% colspan="1" %)(% colspan="1" %)
189 (((
190 (% style="color:#222222" %)Machine Learning and Automatic Editing
191 )))|(% colspan="1" %)(% colspan="1" %)
192 (((
193 (% class="content-wrapper" %)
194 (((
195
196
197 {{view-file att--filename="Abstract 2021_Ngedangenzwa_Tolsheden.docx" height="150"/}}
198 )))
199 )))
200 |(% colspan="1" %)(% colspan="1" %)
201 (((
202 **Mykola Sydorov** (Taras Shevchenko National University of Kyiv, Ukraine) and(% style="color:#000000" %) **Oleksiy Sereda** (Taras Shevchenko National University of Kyiv, Ukraine)
203 )))|(% colspan="1" %)(% colspan="1" %)
204 (((
205 (% style="color:#000000" %)UniDOS online with LimeSurvey
206 )))|(% colspan="1" %)(% colspan="1" %)
207 (((
208 (% class="content-wrapper" %)
209 (((
210
211
212 {{view-file att--filename="Sydorov.doc" height="150"/}}
213 )))
214 )))
215
216 == Contributed papers ==
217
218 (% class="relative-table wrapped" style="width:100.0%" %)
219 |=(((
220 Speaker
221 )))|=(((
222 Title
223 )))|=(((
224 Abstract
225 )))
226 |(((
227 (% style="color:#000000" %)**Vilma Nekrašaitė-Liegė** (Vilnius Gediminas technical University and Statistics Lithuania, Lithuania)
228 )))|(((
229 A COMPARISON OF URL FINDERS FOR ONLINE-BASED ENTERPRISE CHARACTERISTICS
230 )))|(((
231 (% class="content-wrapper" %)
232 (((
233
234
235 {{view-file att--filename="BNU_2021_Vilma_Liege.pdf" height="150"/}}
236 )))
237 )))
238 |(((
239 (% style="color:#000000" %)**Milda Šličkutė-Šeštokienė **(Statistics Lithuania, Lithuania)
240 )))|(((
241 Register based census in Lithuania
242 )))|(((
243 (% class="content-wrapper" %)
244 (((
245
246
247 {{view-file att--filename="Abstract - Milda Slickute.docx" height="150"/}}
248 )))
249 )))
250 |(((
251 (% style="color:#000000" %)**Ieva Burakauskaitė** (Statistics Lithuania, Lithuania) **and Vilma Nekrašaitė-Liegė** (Vilnius Gediminas technical University and Statistics Lithuania, Lithuania)
252 )))|(((
253 Selective Editing Using Contamination Model
254 )))|(((
255 (% class="content-wrapper" %)
256 (((
257
258
259 {{view-file att--filename="Contributed paper_I. Burakauskaite and V. Nekrasaite-Liege.pdf" height="150"/}}
260 )))
261 )))
262 |(% colspan="1" %)(% colspan="1" %)
263 (((
264 **Baiba Zukula **(Central Statistical Bureau of Latvia, Latvia)
265 )))|(% colspan="1" %)(% colspan="1" %)
266 (((
267 CAWI-mobile FOR HOUSEHOLD SURVEYS
268 )))|(% colspan="1" %)(% colspan="1" %)
269 (((
270 (% class="content-wrapper" %)
271 (((
272
273
274 {{view-file att--filename="Abstracts_SummerSchool2021_BZ.docx" height="150"/}}
275 )))
276 )))
277 |(% colspan="1" %)(% colspan="1" %)
278 (((
279 (% style="color:#000000" %)**Ruāna Pavasare **(Central Statistical Bureau, Latvia)
280 )))|(% colspan="1" %)(% colspan="1" %)
281 (((
282 (% style="color:#000000" %)Statistical Editing and Imputation of Missing values for the Population Census 2021 in Latvia
283 )))|(% colspan="1" %)(% colspan="1" %)
284 (((
285 (% class="content-wrapper" %)
286 (((
287
288
289 {{view-file att--filename="editing_imputation_RPavasare.doc" height="150"/}}
290 )))
291 )))
292 |(% colspan="1" %)(% colspan="1" %)
293 (((
294 (% style="color:#000000" %)**Ance Cerina** (Central Statistical Bureau of Latvia, Latvia)** and Zane Matveja **(Central Statistical Bureau of Latvia, Latvia)
295 )))|(% colspan="1" %)(% colspan="1" %)
296 (((
297 (% style="color:#000000" %)Statistical Disclosure Control for Census 2021
298 )))|(% colspan="1" %)(% colspan="1" %)
299 (((
300 (% class="content-wrapper" %)
301 (((
302
303
304 {{view-file att--filename="BNU_SDC_CENSUS_Latvia_abstract.docx" height="150"/}}
305 )))
306 )))
307 |(% colspan="1" %)(% colspan="1" %)
308 (((
309 (% style="color:#000000" %)**Jelena Voronova** (Central statistical bureau of Latvia, Latvian University, Latvia)
310 )))|(% colspan="1" %)(% colspan="1" %)
311 (((
312 (% style="color:#000000" %)OBSERVING NONRESPONSE BIAS AND OPTIMISING DATA COLLECTION STRATEGY FOR ADAPTIVE SAMPLE SURVEY DESIGN
313 )))|(% colspan="1" %)(% colspan="1" %)
314 (((
315 (% class="content-wrapper" %)
316 (((
317
318
319 {{view-file att--filename="BNU2021JVoronova-Observing-nonresponse-bias-and-optimising-data-collection-strategy-for-adaptive-sample-survey-design.doc" height="150"/}}
320 )))
321 )))
322 |(% colspan="1" %)(% colspan="1" %)
323 (((
324 (% style="color:#000000" %)**Ulrich Rendtel, Andreas Neudecker and Lukas Fuchs**
325 )))|(% colspan="1" %)(% colspan="1" %)
326 (((
327 (% style="color:#000000" %)The display of Corona incidences in space and time
328 )))|(% colspan="1" %)(% colspan="1" %)
329 (((
330 (% class="content-wrapper" %)
331 (((
332
333
334 {{view-file att--filename="Abstract_Banocoss_2021_Rendtel.pdf" height="150"/}}
335 )))
336 )))
337 |(% colspan="1" %)(% colspan="1" %)
338 (((
339 (% style="color:#000000" %)**Liliāna Roze** (Central Statistical Bureau of Latvia, Latvia)
340 )))|(% colspan="1" %)(% colspan="1" %)
341 (((
342 (% style="color:#000000" %)Optimal identification of auxiliary variables in sample surveys to reduce nonresponse bias
343 )))|(% colspan="1" %)(% colspan="1" %)
344 (((
345 (% class="content-wrapper" %)
346 (((
347
348
349 {{view-file att--filename="BNU_LRoze_optimal_identification_of_auxiliary_variables_to_reduce_nonresponse_bias.docx" height="150"/}}
350 )))
351 )))
352 |(% colspan="1" %)(% colspan="1" %)
353 (((
354 (% style="color:#000000" %)**Mārtiņš Liberts** (Central Statistical Bureau of Latvia, Latvia)
355 )))|(% colspan="1" %)(% colspan="1" %)
356 (((
357 (% style="color:#000000" %)Unequal Probability Sampling for the European Interview Health Survey in Latvia
358 )))|(% colspan="1" %)(% colspan="1" %)
359 (((
360 (% class="content-wrapper" %)
361 (((
362
363
364 {{view-file att--filename="BNU2021-CP-Martins-Liberts.pdf" height="150"/}}
365 )))
366 )))
367 |(% colspan="1" %)(% colspan="1" %)
368 (((
369 (% style="color:#000000" %)**Darja Goreva** (Central StatisticalBureau of Latvia, Latvia)** and Viktors Veretjanovs **(Central StatisticalBureau of Latvia, Latvia)
370 )))|(% colspan="1" %)(% colspan="1" %)
371 (((
372 (% style="color:#000000" %)ANALYSIS OF EU-SILC DATA DEPENDING ON MODES OF DATA COLLECTION IN LATVIA
373 )))|(% colspan="1" %)(% colspan="1" %)
374 (((
375 (% class="content-wrapper" %)
376 (((
377
378
379 {{view-file att--filename="ANALYSIS OF EU-SILC DATA DEPENDING ON MODES OF DATA COLLECTION IN LATVIA_DGoreva_VVeretjanovs.doc" height="150"/}}
380 )))
381 )))
382 |(% colspan="1" %)(% colspan="1" %)
383 (((
384 (% style="color:#000000" %)**Danute Krapavickaite**
385 )))|(% colspan="1" %)(% colspan="1" %)
386 (((
387 (% style="color:#000000" %)Highlights of the WSC 2021 in Survey Statistics
388 )))|(% colspan="1" %)(% colspan="1" %)
389 (((
390 (% class="content-wrapper" %)
391 (((
392
393
394 {{view-file att--filename="Krapavickaite_abstract_in_template_UPDATED.pdf" height="150"/}}
395 )))
396 )))
397 |(% colspan="1" %)(% colspan="1" %)
398 (((
399 (% style="color:#000000" %)**Yana Bondarenko**
400 )))|(% colspan="1" %)(% colspan="1" %)
401 (((
402 A Sequential Probability Ratio Test for Online Experiments
403 )))|(% colspan="1" %)(% colspan="1" %)
404 (((
405 (% class="content-wrapper" %)
406 (((
407
408
409 {{view-file att--filename="Yana Bondarenko A Sequential Probability Ratio Test for Online Experiments.doc" height="150"/}}
410 )))
411 )))
412 |(% colspan="1" %)(% colspan="1" %)
413 (((
414 (% style="color:#000000" %)**Andrius Čiginas**
415 )))|(% colspan="1" %)(% colspan="1" %)
416 (((
417 (% style="color:#000000" %)On design mean square error estimation for model-based small area estimators
418 )))|(% colspan="1" %)(% colspan="1" %)
419 (((
420 (% class="content-wrapper" %)
421 (((
422
423
424 {{view-file att--filename="Ciginas.pdf" height="150"/}}
425 )))
426 )))
427 |(% colspan="1" %)(% colspan="1" %)
428 (((
429 **Natalia Bokun**
430 )))|(% colspan="1" %)(% colspan="1" %)
431 (((
432 (% style="color:#000000" %)Enterprises Survey on Personnel Demand
433 )))|(% colspan="1" %)(% colspan="1" %)
434 (((
435 (% class="content-wrapper" %)
436 (((
437
438
439 {{view-file att--filename="Natalia Bokun Abstract.doc" height="150"/}}
440 )))
441 )))
442 |(% colspan="1" %)(% colspan="1" %)
443 (((
444 **Maria Valaste and Hanna Wass**
445 )))|(% colspan="1" %)(% colspan="1" %)
446 (((
447 (% style="color:#000000" %)Data Collection Mode and Nonresponse: Practical Experiences
448 )))|(% colspan="1" %)(% colspan="1" %)
449 (((
450 (% class="content-wrapper" %)
451 (((
452
453
454 {{view-file att--filename="VALASTE_WASS.pdf" height="150"/}}
455 )))
456 )))
457 |(% colspan="1" %)(% colspan="1" %)
458 (((
459 (% style="color:#000000" %)**Anastasiia Volkova**
460 )))|(% colspan="1" %)(% colspan="1" %)
461 (((
462 (% style="color:#000000" %)On the importance of conceptualization and operationalization in survey design: lessons from the Morally Debatable Behaviors scale
463 )))|(% colspan="1" %)(% colspan="1" %)
464 (((
465 (% class="content-wrapper" %)
466 (((
467
468
469 {{view-file att--filename="Volkova_abstract_updated.doc" height="150"/}}
470 )))
471 )))
472 |(% colspan="1" %)(% colspan="1" %)
473 (((
474 **Sakovich N.**
475 )))|(% colspan="1" %)(% colspan="1" %)
476 (((
477 Consumer Prices Sample Surveys in Belarus / Выборочные обследования потребительских цен в Беларуси (Contributed paper for Russian Saturday)
478 )))|(% colspan="1" %)(% colspan="1" %)
479 (((
480 (% class="content-wrapper" %)
481 (((
482
483
484 {{view-file att--filename="Sakovich N Abstract.doc" height="150"/}}
485 )))
486 )))
487 |(% colspan="1" %)(% colspan="1" %)
488 (((
489 **Natalia Bokun**
490 )))|(% colspan="1" %)(% colspan="1" %)
491 (((
492 Labor Market Surveys in Belarus / Выборочные обследования на рынке труда в Беларуси (Contributed paper for Russian Saturday)
493 )))|(% colspan="1" %)(% colspan="1" %)
494 (((
495 (% class="content-wrapper" %)
496 (((
497
498
499 {{view-file att--filename="Bokun abstract in Russian.doc" height="150"/}}
500 )))
501 )))
502 |(% colspan="1" %)(% colspan="1" %)
503 (((
504 **Sharilova Eugenia**
505 )))|(% colspan="1" %)(% colspan="1" %)
506 (((
507 Sample surveys in assessing the main determinants of fertility decline in the Republic of Belarus /  Выборочные обследования в оценке основных детерминантов снижения рождаемости в Республике Беларусь (Contributed paper for Russian Saturday)
508 )))|(% colspan="1" %)(% colspan="1" %)
509 (((
510 (% class="content-wrapper" %)
511 (((
512
513
514 {{view-file att--filename="Шарилова - тезисы.docx" height="150"/}}
515 )))
516 )))
517 |(% colspan="1" %)(% colspan="1" %)
518 (((
519 **Korolenok A.**
520 )))|(% colspan="1" %)(% colspan="1" %)
521 (((
522 PROBLEMS OF SURVEY OF UNPAID ACTIVITIES (Contributed paper for Russian Saturday)
523 )))|(% colspan="1" %)(% colspan="1" %)
524 (((
525 (% class="content-wrapper" %)
526 (((
527
528
529 {{view-file att--filename="Korolenok.doc" height="150"/}}
530 )))
531 )))
532 |(% colspan="1" %)(% colspan="1" %)
533 (((
534 **Nataliya Pekarskaya**
535 )))|(% colspan="1" %)(% colspan="1" %)
536 (((
537 HOUSEHOLDS SURVEY TO MEASURE THE AGRICULTURAL ACTIVITY (Contributed paper for Russian Saturday)
538 )))|(% colspan="1" %)(% colspan="1" %)
539 (((
540 (% class="content-wrapper" %)
541 (((
542
543
544 {{view-file att--filename="Pekarskaya - 2021.pdf" height="150"/}}
545 )))
546 )))
547 |(% colspan="1" %)(% colspan="1" %)
548 (((
549 **Liudmila Soshnikava**
550 )))|(% colspan="1" %)(% colspan="1" %)
551 (((
552 Using logistic regression to analyze the results of statistical observations
553 )))|(% colspan="1" %)(% colspan="1" %)
554 (((
555 (% class="content-wrapper" %)
556 (((
557
558
559 {{view-file att--filename="Summa school_soshnikava.docx" height="150"/}}
560 )))
561 )))
562
563 ~-~--
564
565 //Last update 13 September 2021//