Open Access Publications from the University of California

## Department of Statistics Papers

The Department of Statistics at UCLA coordinates undergraduate and graduate statistics teaching and research within the College of Letters and Sciences. We teach a large number of undergraduates and we have a substantial graduate program. Our research and teaching have a strong emphasis on computational and applied statistics.

(2023)

## Converting Statistical Literacy Resources to Data Science Resources

(2023)

Data Science is considered a pseudonym for handling big data, machine learning, statistics, computing and mathematics. It is no uncommon for learners to think that all that requires a radical change in their education and even a change in the name of their Statistics major. However, it is not too difficult for a program promoting statistical literacy to at the same time use its resources as data science resources. It takes translation, some acquaintance with what all practitioners of data science usually do, and a willingness to edit the resources to make learners feel that they are immersed in the data science world.

## CGM and insulin pump data to introduce classical and machine learning time series analysis concepts to students

(2021)

The case study engages students and makes them use the tools they know to investigate a complex process. At the same time, they learn basic time-series concepts using only their intro stats tools.

## Women mathematicians in data-centric occupations (with a context)

(2020)

Texas State University, Women Doing Math and Talk Math 2 Me Joint Statistics Seminar

• 1 supplemental PDF

(2017)

(2017)

## Sensitivity of Econometric Estimates to Item Non-response Adjustment

(2016)

Non-response in establishment surveys is a very important problem that can bias results of statistical analysis. The bias can be considerable when the survey data is used to do multivariate analysis that involve several variables with different response rates, which can reduce the effective sample size considerably. Fixing the non-response, however, could potentially cause other econometric problems. This paper uses an operational approach to analyze the sensitivity of results of multivariate analysis to multiple imputation procedures applied to the U.S. Census Bureau/NSF‘s Business Research and Development and Innovation Survey (BRDIS) to address item non-response. Multiple imputation is first applied using data from all survey units and periods for which there is data, presenting scenario 1. A scenario 2 involves separate imputation for units that have participated in the survey only once and those that repeat. Scenario 3 involves no imputation. Sensitivity analysis is done by comparing the model estimates and their standard errors, and measures of the additional uncertainty created by the imputation procedure. In all cases, unit non-response is addressed by using the adjusted weights that accompany BRDIS micro data. The results suggest that substantial benefit may be derived from multiple imputation, not only because it helps provide more accurate measures of the uncertainty due to item non-response but also because it provides alternative estimates of effect sizes and population totals.

(2015)

(2015)

## Innovation Output Choices and Characteristics of Firms in the U.S.

(2014)

This paper uses new business micro data from the Business Research and

Development and Innovation Survey (BRDIS) for the years 2008-2011 to relate

the discrete innovation choices made by U.S. companies to features of the com-

pany that have long been considered to be important correlates of innovation.

We use multinomial logit to model those choices. Bloch and Lopez-Bassols

(2009) used the Community Innovation Surveys (CIS) to classify companies

according dual, technological or output-based innovation constructs. We found

that for each of those constructs of innovation combinations considered, man-

ufacturing and engaging in intellectual property transfer increase the odds

of choosing innovation strategies that involve more than one type of cate-

gories (for example, both goods and services, or both tech and non-tech) and

radical innovations, controlling for rm size, productivity, time and type of

R&D. Company size and company productivity as well as time do not lean

the choices in any particular direction. These associations are robust across

the three multinomial choice models that we have considered. In contrast with

other studies, we have been able to use companies that do and companies that

do not innovate, and this has allowed to rule out to some extent selectivity

bias.