A commonly-used and naive solution to process data with missing attribute values is to ignore the instances which contain missing attribute values. This method may neglect important information within the data, significant amount of data could be easily discarded, and the discovered knowledge may not contain significant rules. Some methods, such as assigning the most common values or assigning ...

Empirical work in macroeconomics almost universally relies on the hypothesis of rational expectations. This paper departs from the literature by considering a variety of alternative expectations formation models. We study the econometric properties of a popular New Keynesian monetary DSGE model under different expectational assumptions: the benchmark case of rational expectations, rational expe...

In the Colored Bin Packing problem a sequence of items of sizes up to 1 arrives to be packed into bins of unit capacity. Each item has one of c ≥ 2 colors and an additional constraint is that we cannot pack two items of the same color next to each other in the same bin. The objective is to minimize the number of bins. In the important special case when all items have size zero, we characterize ...

The Pearson-Fisher chi-squared test can be used to evaluate the goodnessof-fit of categorized continuous data with known bin endpoints compared to a continuous distribution, in the presence of unknown (nuisance) distribution parameters. Rayner and McAlevey [11] and Rayner and Best [9],[10] demonstrate that in this case, component tests of the Pearson-Fisher chi-squared test statistic can be obt...

Least-squares means are predictions from a linear model, or averages thereof. They are useful in the analysis of experimental data for summarizing the effects of factors, and for testing linear contrasts among predictions. The lsmeans package provides a simple way of obtaining least-squares means and contrasts thereof. It supports many models fitted by R core packages (as well as a few key cont...

Many imaging and compressed sensing applications seek sparse solutions to large under-determined least-squares problems. The basis pursuit (BP) approach minimizes the 1-norm of the solution, and the BP denoising (BPDN) approach balances it against the least-squares fit. The duals of these problems are conventional linear and quadratic programs. We introduce a modified parameterization of the BP...

Asymptotically optimal correlation structure methods with binary data can break down in small samples. A new correlation structure methodology based on a recently developed odds-ratio (OR) approximation to the tetrachoric correlation coefficient is proposed as an alternative to the LPB approach proposed by Lee et al. (1995). Unweighted least squares (ULS) estimation with robust standard errors ...

Objective: The purpose of this study is to examine the effect of Auditors’ Individual Differences on their Professional Commitment. Also, the mediation effect of Ethical Climate Fit has been examined. Methods: According to the Social Cognitive Theory, two criteria of Internal Locus of Control and Self-Efficacy were used as Auditors’ Individual Differences. To measure the Internal Locus of Cont...

The title compound, C(48)H(40), is a tetra-isopropyl-substituted polyannulenoenyne. The unsubstituted polyannulenoenyne, C(36)H(16) (CSD: RICVEE; CAS: 186494-87-1), has quasi-D(2) (222) symmetry, as determined by least-squares fit (excluding H atoms) to a model optimized in D(2) symmetry by mol-ecular mechanics (r.m.s. deviation = 0.239 Å). The least-squares fits of 36 common C atoms of the tit...

In this contribution, we discuss a robust estimator based on image reconstruction technique for image filtering and simplification purposes. Instead of using the least-squares estimators that the measurement error is independently random and distributed as a normal distribution, a Lorentzian distribution based estimator is employed to fit a model function to the input image within the local win...