Ownership structure and investment finance in transition economies:a survey of evidence from large firms in Hungary and Poland

Using survey data on 157 large private Hungarian and Polish companies this paper investigates links between ownership structures and CEOs’ expectations with regard to sources of finance for investment. The Bayesian estimation is used to deal with the small sample restrictions, while classical methods provide robustness checks. We found a hump-shaped relationship between ownership concentration and expectations of relying on public equity. The latter is most likely for firms where the largest investor owns between 25 percent and 49 percent of shares, just below the legal control threshold. More profitable firms rely on retained earnings for their investment finance, consistent with the ‘pecking order’ theory of financing. Finally, firms for which the largest shareholder is a domestic institutional investor are more likely to borrow from domestic banks.

Ownership characteristics and access to finance:evidence from a survey of large privatised companies in Hungary and Poland

We examine financial constraints and forms of finance used for investment, by analysing survey data on 157 large privatised companies in Hungary and Poland for the period 1998 - 2000. The Bayesian analysis using Gibbs sampling is carried out to obtain inferences about the sample companies' access to finance from a model for categorical outcome. By applying alternative measures of financial constraints we find that foreign companies, companies that are part of domestic industrial groups and enterprises with concentrated ownership are all less constrained in their access to finance. Moreover, we identify alternative modes of finance since different corporate control and past performance characteristics influence the sample firms' choice of finance source. In particular, while being industry-specific, the access to domestic credit is positively associated with company size and past profitability. Industrial group members tend to favour bond issues as well as sells-offs of assets as appropriate types of finance for their investment programmes. Preferences for raising finance in the form of equity are associated with share concentration in a non-monotonic way, being most prevalent in those companies where the dominant owner holds 25%-49% of shares. Close links with a leading bank not only increase the possibility of bond issues but also appear to facilitate access to non-banking sources of funds, in particular, to finance supplied by industrial partners. Finally, reliance on state finance is less likely for the companies whose profiles resemble the case of unconstrained finance, namely, for companies with foreign partners, companies that are part of domestic industrial groups and companies with a strategic investor. Model implications also include that the use of state funds is less likely for Polish than for Hungarian companies.

Estimation of IRT Parameters over a Small Sample. Bootstrapping of the Item Responses

2000 Mathematics Subject Classification: 97C40.

The effect of vocational education on high school students in a large urban multicultural school district

The completion of and graduation from high school is a major problem that must be resolved. A 6% random sample of the students (1,059) who were in the 9th grade in Miami-Dade Public Schools, a large urban school district, September 1992, were selected for this study. The sample was divided into 2 groups, advanced academic and general track students. Each group was then divided into vocational and non-vocational. A causal comparative design was used to evaluate the results for graduate vs. non-graduate. The indicators were the program of study, attendance, standardized test scores, grade point average, ethnicity and gender. It was found that both advanced academic and general track students had significantly higher graduation rates at the .01 level when a vocational education program was part of their studies. All of the other indicators did not show any significant differences. If we arc to improve students educational outcomes and reduce the dropout rate, vocational education should be part of every student's education.^

Spatial scale and abundance patterns of large fish communities in freshwater marshes of the Florida Everglades

Anthropogenic habitat alterations and water-management practices have imposed an artificial spatial scale onto the once contiguous freshwater marshes of the Florida Everglades. To gain insight into how these changes may affect biotic communities, we examined whether variation in the abundance and community structure of large fishes (SL . 8 cm) in Everglades marshes varied more at regional or intraregional scales, and whether this variation was related to hydroperiod, water depth, floating mat volume, and vegetation density. From October 1997 to October 2002, we used an airboat electrofisher to sample large fishes at sites within three regions of the Everglades. Each of these regions is subject to unique watermanagement schedules. Dry-down events (water depth , 10 cm) occurred at several sites during spring in 1999, 2000, 2001, and 2002. The 2001 dry-down event was the most severe and widespread. Abundance of several fishes decreased significantly through time, and the number of days post-dry-down covaried significantly with abundance for several species. Processes operating at the regional scale appear to play important roles in regulating large fishes. The most pronounced patterns in abundance and community structure occurred at the regional scale, and the effect size for region was greater than the effect size for sites nested within region for abundance of all species combined, all predators combined, and each of the seven most abundant species. Non-metric multi-dimensional scaling revealed distinct groupings of sites corresponding to the three regions. We also found significant variation in community structure through time that correlated with the number of days post-dry-down. Our results suggest that hydroperiod and water management at the regional scale influence large fish communities of Everglades marshes.

A longitudinal exploration of the relationship between oral reading fluency and reading comprehension achievement among a sample of diverse young learners

Exploring the relationship between early oral reading fluency ability and reading comprehension achievement among an ethnically and racially diverse sample of young learners from low-income families, attending elementary school within a large public school district in southeast Florida is the purpose of this longitudinal study. Although many studies have been conducted to address the relationship between oral reading fluency ability and reading comprehension achievement, most of the existing research failed either to disaggregate the data by demographic subgroups or secure a large enough sample of students to adequately represent the diverse subgroups. The research questions that guided this study were: (a) To what extent does early oral reading fluency ability measured in first, second, or third grade correlate with reading comprehension achievement in third grade? (b) To what extent does the relationship of early oral reading fluency ability and reading comprehension achievement vary by demographic subgroup membership (i.e., gender, race/ethnicity, socioeconomic status) among a diverse sample of students? A predictive research design using archived secondary data was employed in this nonexperimental quantitative methods study of 1,663 third grade students who attended a cohort of 25 Reading First funded schools. The data analyzed derived from the Dynamic Indicators of Basic Early Literacy Skills Oral Reading Fluency (DIBELS ORF) measure administered in first, second, and third grades and the Florida Comprehensive Assessment Test of the Sunshine State Standards (FCAT-SSS) Reading administered in third grade. Linear regression analyses between each of the oral reading fluency and reading comprehension measures produced significant positive correlations. Hierarchical regression analyses supported the predictive potential of all three oral reading fluency ability measures toward reading comprehension achievement, with the first grade oral reading fluency ability measure explaining the most significant variance in third grade reading comprehension achievement. Male students produced significant overall differences in variance when compared to female students as did the Other student subgroup (i.e., Asian, Multiracial, and Native American) when compared to Black, White, and Hispanic students. No significant differences in variance were produced between students from low and moderate socioeconomic families. These findings are vital toward adding to the literature of diverse young learners.

Risk -taking propensity of faculty, administrators, and academic department heads at a large, urban, multi -campus community college

This research was undertaken to explore dimensions of the risk construct, identify factors related to risk-taking in education, and study risk propensity among employees at a community college. Risk-taking propensity (RTP) was measured by the 12-item BCDQ, which consisted of personal and professional risk-related situations balanced for the money, reputation, and satisfaction dimensions of the risk construct. Scoring ranged from 1.00 (most cautious) to 6.00 (most risky).^ Surveys including the BCDQ and seven demographic questions relating to age, gender, professional status, length of service, academic discipline, highest degree, and campus location were sent to faculty, administrators, and academic department heads. A total of 325 surveys were returned, resulting in a 66.7% response rate. Subjects were relatively homogeneous for age, length of service, and highest degree.^ Subjects were also homogeneous for risk-taking propensity: no substantive differences in RTP scores were noted within and among demographic groups, with the possible exception of academic discipline. The mean RTP score for all subjects was 3.77, for faculty was 3.76, for administrators was 3.83, and for department heads was 3.64.^ The relationship between propensity to take personal risks and propensity to take professional risks was tested by computing Pearson r correlation coefficients. The relationships for the total sample, faculty, and administrator groups were statistically significant, but of limited practical significance. Subjects were placed into risk categories by dividing the response scale into thirds. A 3 x 3 factorial ANOVA revealed no interaction effects between professional status and risk category with regard to RTP score. A discriminant analysis showed that a seven-factor model was not effective in predicting risk category.^ The homogeneity of the study sample and the effect of a risk-encouraging environment were discussed in the context of the community college. Since very little data on risk-taking in education is available, risk propensity data from this study could serve as a basis for comparison to future research. Results could be used by institutions to plan professional development activities, designed to increase risk-taking and encourage active acceptance of change. ^

The Effect of Sample and Sample Matrix on DNA Processing: Mechanisms for the Detection and Management of Inhibition in Forensic Samples

The presence of inhibitory substances in biological forensic samples has, and continues to affect the quality of the data generated following DNA typing processes. Although the chemistries used during the procedures have been enhanced to mitigate the effects of these deleterious compounds, some challenges remain. Inhibitors can be components of the samples, the substrate where samples were deposited or chemical(s) associated to the DNA purification step. Therefore, a thorough understanding of the extraction processes and their ability to handle the various types of inhibitory substances can help define the best analytical processing for any given sample. A series of experiments were conducted to establish the inhibition tolerance of quantification and amplification kits using common inhibitory substances in order to determine if current laboratory practices are optimal for identifying potential problems associated with inhibition. DART mass spectrometry was used to determine the amount of inhibitor carryover after sample purification, its correlation to the initial inhibitor input in the sample and the overall effect in the results. Finally, a novel alternative at gathering investigative leads from samples that would otherwise be ineffective for DNA typing due to the large amounts of inhibitory substances and/or environmental degradation was tested. This included generating data associated with microbial peak signatures to identify locations of clandestine human graves. Results demonstrate that the current methods for assessing inhibition are not necessarily accurate, as samples that appear inhibited in the quantification process can yield full DNA profiles, while those that do not indicate inhibition may suffer from lowered amplification efficiency or PCR artifacts. The extraction methods tested were able to remove >90% of the inhibitors from all samples with the exception of phenol, which was present in variable amounts whenever the organic extraction approach was utilized. Although the results attained suggested that most inhibitors produce minimal effect on downstream applications, analysts should practice caution when selecting the best extraction method for particular samples, as casework DNA samples are often present in small quantities and can contain an overwhelming amount of inhibitory substances.

Risk-taking propensity of faculty, administrators and academic departament heads at large, urban, multicampus community college

This research was undertaken to explore dimensions of the risk construct, identify factors related to risk-taking in education, and study risk propensity among employees at a community college. Risk-taking propensity (RTP) was measured by the 12-item BCDQ, which consisted of personal and professional risk-related situations balanced for the money, reputation, and satisfaction dimensions of the risk construct. Scoring ranged from 1.00 (most cautious) to 6.00 (most risky). Surveys including the BCDQ and seven demographic questions relating to age, gender, professional status, length of service, academic discipline, highest degree, and campus location were sent to faculty, administrators, and academic department heads. A total of 325 surveys were returned, resulting in a 66.7% response rate. Subjects were relatively homogeneous for age, length of service, and highest degree. Subjects were also homogeneous for risk-taking propensity: no substantive differences in RTP scores were noted within and among demographic groups, with the possible exception of academic discipline. The mean RTP score for all subjects was 3.77, for faculty was 3.76, for administrators was 3.83, and for department heads was 3.64. The relationship between propensity to take personal risks and propensity to take professional risks was tested by computing Pearson r correlation coefficients. The relationships for the total sample, faculty, and administrator groups were statistically significant, but of limited practical significance. Subjects were placed into risk categories by dividing the response scale into thirds. A 3 X 3 factorial ANOVA revealed no interaction effects between professional status and risk category with regard to RTP score. A discriminant analysis showed that a seven-factor model was not effective in predicting risk category. The homogeneity of the study sample and the effect of a risk encouraging environment were discussed in the context of the community college. Since very little data on risk-taking in education is available, risk propensity data from this study could serve as a basis for comparison to future research. Results could be used by institutions to plan professional development activities, designed to increase risk-taking and encourage active acceptance of change.

A Comparison of Anchor-Item Designs for the Concurrent Calibration of Large Banks of Likert-Type Items

Current interest in measuring quality of life is generating interest in the construction of computerized adaptive tests (CATs) with Likert-type items. Calibration of an item bank for use in CAT requires collecting responses to a large number of candidate items. However, the number is usually too large to administer to each subject in the calibration sample. The concurrent anchor-item design solves this problem by splitting the items into separate subtests, with some common items across subtests; then administering each subtest to a different sample; and finally running estimation algorithms once on the aggregated data array, from which a substantial number of responses are then missing. Although the use of anchor-item designs is widespread, the consequences of several configuration decisions on the accuracy of parameter estimates have never been studied in the polytomous case. The present study addresses this question by simulation, comparing the outcomes of several alternatives on the configuration of the anchor-item design. The factors defining variants of the anchor-item design are (a) subtest size, (b) balance of common and unique items per subtest, (c) characteristics of the common items, and (d) criteria for the distribution of unique items across subtests. The results of this study indicate that maximizing accuracy in item parameter recovery requires subtests of the largest possible number of items and the smallest possible number of common items; the characteristics of the common items and the criterion for distribution of unique items do not affect accuracy.

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

Large magnetoelectric coupling in nanoscale BiFeO3 from direct electrical measurements

We report the results of direct measurement of remanent hysteresis loops on nanochains of BiFeO3 at room temperature under zero and ∼20 kOe magnetic field. We noticed a suppression of remanent polarization by nearly ∼40% under the magnetic field. The powder neutron diffraction data reveal significant ion displacements under a magnetic field which seems to be the origin of the suppression of polarization. The isolated nanoparticles, comprising the chains, exhibit evolution of ferroelectric domains under dc electric field and complete 180 switching in switching-spectroscopy piezoresponse force microscopy. They also exhibit stronger ferromagnetism with nearly an order of magnitude higher saturation magnetization than that of the bulk sample. These results show that the nanoscale BiFeO3 exhibits coexistence of ferroelectric and ferromagnetic order and a strong magnetoelectric multiferroic coupling at room temperature comparable to what some of the type-II multiferroics show at a very low temperature.

(Table 1) Sample descriptions, carbon contents, and lipid yields across the K/T boundary at DSDP Hole 93-605 and at Stevns Klint, Denmark

This reconnaissance study was undertaken to determine whether the mass extinctions and faunal successions that mark the Cretaceous/Tertiary (K/T) boundary left a discernible molecular fossil record in the sediments of this period. Lipid signatures of sediments taken from above and below the K/T boundary were compared in core and outcrop samples taken from two locations: the U.S. east coast continental margin (western Atlantic Ocean, DSDP Site 605) and Stevns Klint, Denmark. Four calcareous sediments taken from above and below the K/T boundary in DSDP Hole 605, Section 605-66-1, revealed changing lipid signatures between above and below that are characterized by a large component of unresolved naphthenic hydrocarbons and a homologous series of n-alkanes ranging from Ci6 to C33. These lipid signatures are attributed to an influx of a terrestrial higher plant component and to bacterial reworking of the sediments under partially anoxic depositional and/or diagenetic conditions. The outcrop samples from Stevns Klint had extremely low concentrations of indigenous lipids. The fish clay at the K/T boundary contained traces of microbial hydrocarbons and fatty acids, whereas the carbonates above and below had only microbial fatty acids and additional terrestrial resin acids. The data from both sites indicate a perturbation in the deposition of lipid compound classes across the K/T boundary.

Geochemistry of basalts from the Caribbean Large Igneous Province

Oceanic flood basalts are poorly understood, short-term expressions of highly increased heat flux and mass flow within the convecting mantle. The uniqueness of the Caribbean Large Igneous Province (CLIP, 92-74 Ma) with respect to other Cretaceous oceanic plateaus is its extensive sub-aerial exposures, providing an excellent basis to investigate the temporal and compositional relationships within a starting plume head. We present major element, trace element and initial Sr-Nd-Pb isotope composition of 40 extrusive rocks from the Caribbean Plateau, including onland sections in Costa Rica, Colombia and Curaçao as well as DSDP Sites in the Central Caribbean. Even though the lavas were erupted over an area of ~3*10**6 km**2, the majority have strikingly uniform incompatible element patterns (La/Yb=0.96+/-0.16, n=64 out of 79 samples, 2sigma) and initial Nd-Pb isotopic compositions (e.g. 143Nd/144Ndin=0.51291+/-3, epsilon-Nd i=7.3+/-0.6, 206Pb/204Pbin=18.86+/-0.12, n=54 out of 66, 2sigma). Lavas with endmember compositions have only been sampled at the DSDP Sites, Gorgona Island (Colombia) and the 65-60 Ma accreted Quepos and Osa igneous complexes (Costa Rica) of the subsequent hotspot track. Despite the relatively uniform composition of most lavas, linear correlations exist between isotope ratios and between isotope and highly incompatible trace element ratios. The Sr-Nd-Pb isotope and trace element signatures of the chemically enriched lavas are compatible with derivation from recycled oceanic crust, while the depleted lavas are derived from a highly residual source. This source could represent either oceanic lithospheric mantle left after ocean crust formation or gabbros with interlayered ultramafic cumulates of the lower oceanic crust. High 3He/4He in olivines of enriched picrites at Quepos are ~12 times higher than the atmospheric ratio suggesting that the enriched component may have once resided in the lower mantle. Evaluation of the Sm-Nd and U-Pb isotope systematics on isochron diagrams suggests that the age of separation of enriched and depleted components from the depleted MORB source mantle could have been <=500 Ma before CLIP formation and interpreted to reflect the recycling time of the CLIP source. Mantle plume heads may provide a mechanism for transporting large volumes of possibly young recycled oceanic lithosphere residing in the lower mantle back into the shallow MORB source mantle.