More specifically, we use Theorem B.1 from (Sheffet, 2015) that states that given a matrix A whose all of its singular values at greater than T ( , δ) where T ( , δ) = 2B (√ 2r ln(4/δ) + 2 ln(4/δ) ) , publishing RA is ( , δ)differentially private for a r-row matrix R whose entries sampled are i.i.d normal Gaussians. Since we have that all of the singular values of A′ are greater than w (as spec...

Linear regression is one of the most prevalent techniques in machine learning; however, it is also common to use linear regression for its explanatory capabilities rather than label prediction. Ordinary Least Squares (OLS) is often used in statistics to establish a correlation between an attribute (e.g. gender) and a label (e.g. income) in the presence of other (potentially correlated) features...

The purpose of this research is to examine whether outcome controls of group work (i.e. time pressure and reward), trust, and motivation affect shared leadership in virtual teams. In addition, we explore the relationship between shared leadership and information sharing effectiveness in these teams. Results of a laboratory experiment on collaboration technology-based virtual teams indicate that...

Directly solving the ordinary least squares problem will (in general) require O(nd) operations. From Table 5.1, the Gaussian sketch does not actually improve upon this scaling for unconstrained problems: when m d (as is needed in the unconstrained case), then computing the sketch SA requires O(nd) operations as well. If we compute sketches using the JLT, then this cost is reduced to O(nd log(d)...

Previously identified predictors of public punitiveness include attitudinal, experiential, background, and demographic characteristics. Given the influence of parenthood on certain attitudes and beliefs, it may also affect how strongly individuals endorse harsh punishment for criminals. Few studies have explored how parenthood influences general policy preferences or support for criminal justic...

When independent variables have high linear correlation in a multiple linear regression model, we can have wrong analysis. It happens if we do the multiple linear regression analysis based on common Ordinary Least Squares (OLS) method. In this situation, we are suggested to use ridge regression estimator. We conduct some simulation study to compare the performance of ridge regression estimator ...

The present article deals with the problem of estimation of parameters in a linear regression model when some data on response variable is missing and the responses are equicorrelated. The ordinary least squares and optimal homogeneous predictors are employed to nd the imputed values of missing observations. Their eeciency properties are analyzed using the small disturbances asymptotic theory. ...