Optimal designs for mixed continuous and binary responses with quantitative and qualitative factors

Exact [formula omitted]-optimal designs for a second-order response surface model on a circle with qualitative factors

This article uses neural networks in forecasting stock market prices. With their ability to discover patterns in nonlinear and chaotic systems, neural networks offer the ability to predict market directions more accurately than current techniques such as technical analysis, fundamental analysis, and regression compared with neural network performance. Proposed intelligent stock market prediction system is based on the quantitative and qualitative factors. Three feedforward neural models can be used to analyze these factors. Input data to the neural network proposed are quantitative factors. Input data to the neural network proposed for qualitative factors can be factors related to the political effect considered. Third neural network consists of decision integration in which input data will be the outputs of above-mentioned neural networks. This facilitates to make right decision whether stock market is influenced by quantitative or qualitative factors

This paper presents a qualitative and quantitative research to explore and understand the agents that determine the level of productiveness and the importance of qualitative factors. A questionnaire was conducted to estimate the outcome of the qualitative agents or factors in improving the productivity of microtunneling projects. Information was collected from 26 competent professionals in microtunneling projects for almost 20years through an expert's opinion, using a semi-structured questionnaire. The Analytical Hierarchy Process and the clustering analysis technique have been used to account for the twenty qualitative agents or factors concerned in developing a proposed assessment model. The present research has developed an assessment model that stems from the result of qualitative factors on microtunneling to investigate the effect induced by the qualitative agents on productiveness. The assessment model is thereafter confirmed, showing an analytical outcome of 79.85% moderately justified percent. This study proved that the qualitative factors affecting the productivity of microtunneling projects can be assessed through the productivity index concept. This paper helps the industry parties to understand the factors influencing the success of microtunneling projects as well as, development of an assessment model to calculate productivity. Most of the past research on productivity models are based on quantitative factors only. This paper presents qualitative research to explore and understand the factors that affect productivity and the importance of qualitative factors. The developed assessment model presented is the first in connecting theory, analytical methods, and experts' opinions to help practitioners to understand and assess the productivity of microtunneling projects.

A difficulty in joint modeling of continuous and discrete response variables is the lack of a natural multivariate distribution. For joint modeling of clustered observations on binary and continuous responses, we study a correlated probit model that has an underlying normal latent variable for the binary responses. Catalano and Ryan have factored the model into a marginal and a conditional component and used generalized estimating equations methodology to estimate the effects. We propose a Monte Carlo expectation–conditional maximization algorithm for finding maximum likelihood estimates of the mixed model itself, extending and accelerating an algorithm for models with binary responses. We demonstrate the methodology with a developmental toxicity study measuring fetal weight and a binary malformation status for several litters of mice. A simulation study suggests that efficiency gains of joint fittings over separate fittings of the response variables occur mainly for small datasets with strong correlations between the responses within cluster.

: This thesis analyzes the effect of quantitative and qualitative factors used in the admissions process at the U.S. Naval Academy in determining the performance of candidates admitted. In determining student performance, graduation, Order of Merit (OOM), cumulative academic Quality Point Ratio (QPR), cumulative military QPR, and striper selection are used as performance outcome measures. The data is from Naval Academy graduation year groups 1995 through 2001. The analysis separates the Naval Academy's Whole Person Multiple into quantitative and qualitative inputs. The Candidate Multiple (CM) is the quantitative input to the admissions process derived from a statistics-based scoring model anchored in proven high school performance measures such as the SAT and high school GPA. The Recommendations of the Admissions Board (RAB) are the qualitative input, which awards points for subjective traits not captured in the CM or that are from various other subjective measures, such as student interviews and essays. This research highlights the properties of the two admissions factors and their estimated impact on student performance. The results show that student performance increased as CM and RAB increased, revealing the importance of a combined quantitative and qualitative admissions process, and emphasizing qualitative input as the value added factor that provides an increased predictability of student success. (26 tables, 22 figures, 26 refs.)

Risk-Value-Cost-based Optimization of Global Value-adding Structures

The purpose of the study is to form an economic and mathematical instrument for assessing the role of quantitative and qualitative factors in changing the output of dairy products. Objectives: to determine the main theoretical provisions for calculating the indicator of dairy production depending on the resources used; to present a formalized expression for calculating quantitative factors of dairy output growth; to substantiate methodological tools for calculating qualitative, intensive factors of dairy output growth. The object of research is the scientific and technological development of the dairy subcomplex. The economic-mathematical and economic-statistical research methods were used. It has been established that the scientific and technological development of the dairy subcomplex is expressed, first of all, in an increase in the values of qualitative factors, the formal expression of which may be the values of labor productivity, capital productivity, yield or productivity; In periods with a constant level of technical development, quantitative factors receive priority - the number of workers, the cost of basic funds, the acreage, and the number of cows. A toolkit has been developed to assess the cumulative impact of both quantitative and qualitative factors on the resulting indicator of milk production, which will make it possible to reasonably assess the scientific and technological development of the dairy subcomplex. The qualitative factors include the productivity of the workers of the dairy subcomplex, the return on capital and the productivity of cows. Possible combinations of the influence of factors on the resulting indicator of milk production are determined. The key features of changes in influencing variables during periods with a constant level of technical development and in the context of the emergence of new, innovative technological solutions are characterized. Possible directions for further research to assess the scientific and technological de-velopment of the dairy-food subcomplex are outlined.

This paper deals with the optimal design of monolithic piezoelectric microactuators with integrated proprioceptive sensors. Dedicated to the microrobotic and micromechatronic fields, this work details the modelling and the characterization of compliant structures with integrated actuating and sensing elements. The proposed optimal design procedure addresses not only static criteria but also dynamic ones. This leads to microdevices which are better performing with regards to mechanical (displacement, force, etc) and control (dynamics, stability, precision) characteristics. The efficient design of such devices is achieved using a flexible building block method. A topological optimization method combined with an evolutionary algorithm is used to optimize the design of a truss-like planar structure. This method chooses the best location among the different piezoelectric elements. Different mechanical, actuation or sensing elements are accordingly chosen from a data bank. From the control point of view, optimization criteria are considered, to enforce the observability of the vibrational dominant modes of the structure. Therefore, control and observation Gramians are exploited in the optimal design to shape the open-loop frequency response of both actuation and sensing functions of the integrated device. In the last part of the paper, based on these results, the optimal design and manufacture of an innovative piezoelectric flexible microgripper is proposed. The prototype is manufactured from a monolithic piezoelectric material (PIC 151). Its reduced size (15 mm × 18 mm) fits the requirements of both microrobotic and micromechatronic applications, and it is suitable for micromanipulation tasks. Closing the paper, the characterization and the performance of this integrated microactuator and the efficiency of the optimal design procedure for micromechatronic applications are shown.

The multi-criteria facilities layout problem can be formulated as a quadratic assignment model that can handle multiple qualitative and quantitative factors in the objective function. Some studies have shown that the techniques and tools of facilities layout problems can equally be applied for the layout design of user interface components in human-computer interface. This paper presents an alternate approach, which handles multiple qualitative and quantitative factors in a different manner separately in the objective function to obtain the initial layouts. The proposed approach also consists of a layout procedure, in which the pair of facilities with the least composite criterion value has been selected to be placed far apart in the layout to generate an initial layout in the construction procedure. The results of the proposed approach are compared with that of an existing approach which handles a number of qualitative and quantitative factors in the same manner as in the objective function to obtain the initial layouts for the example task of the user interface components layout problem under consideration.

Modeling experiments with qualitative and quantitative factors is an important issue in computer modeling. We propose a framework for building Gaussian process models that incorporate both types of factors. The key to the development of these new models is an approach for constructing correlation functions with qualitative and quantitative factors. An iterative estimation procedure is developed for the proposed models. Modern optimization techniques are used in the estimation to ensure the validity of the constructed correlation functions. The proposed method is illustrated with an example involving a known function and a real example for modeling the thermal distribution of a data center.

Several discrete responses, such as health status, reproduction performance and meat quality, are routinely collected for several livestock species. These traits are often of binary or discrete nature. Genetic evaluation for these traits is frequently conducted using a single-trait threshold model, or they are considered continuous responses either in univariate or in multivariate context. Implementation of threshold models in the presence of several binary responses or a mixture of binary and continuous responses is far from simple. The complexity of such implementation is primarily due to the incomplete randomness of the residual (co)variance matrix. In the current study, a multiple binary trait simulation was carried out in order to implement and validate a new procedure for dealing with the consequences of the restrictions imposed to the residual variance using threshold models. Using three and eight binary responses, the proposed method was able to estimate all unknown parameters without any noticeable bias. In fact, for simulated residual correlations ranging from −0.8 to 0.8, the resulting HPD 95% intervals included the true values in all cases. The proposed procedure involved limited additional computational cost and is straightforward to implement independent of the number of binary responses involved in the analysis. Monitoring of the convergence of the procedure must be conducted at the identifiable scale, and special care must be placed on the selection of the prior of the non-identifiable model. The latter could have serious consequences on the final results due to potential truncation of the parameter space.

29 Review of optimal bayes designs

PurposeThe purpose of this study is to investigate the decision-making process of librarians at the University of Western Ontario who attempted to cancel the Wiley Big Deal. The aim of the study is to reveal the underlying factors that affected their decision-making process. By understanding the decision-making process of librarians, it may be possible to devise a system that takes into consideration not only quantitative factors but also the subjective or qualitative factors that impact librarians’ decisions and thus make it easier to cancel these Big Deals.Design/methodology/approachThe study involved administering an online survey to 25 librarians involved in the cancellation project. Follow-up interviews were conducted with 13 of these librarians to understand at a deeper and more nuanced level the factors that influenced their decisions.FindingsThe main finding was that the librarians who participated in the study could be divided into two groups – a data-driven criteria group and a subjective criteria group – based on their ranking of the factors used to make their cancellation decisions. Most librarians interviewed used a mixture of quantitative factors and qualitative factors when making their cancellation decisions. The authors found that those participants who had greater professional experience and a closer relationship with the faculties in their subject areas had more difficulty in cancelling journals. Very few librarians relied on quantitative data alone.Originality/valueThis study is one of few that have examined the subjective factors that influence librarians’ decisions regarding cancellation of Big Deals. It has implications regarding the movement towards centralized collection management and reliance on quantitative data alone when making collection decisions.

We propose a flexible yet computationally efficient approach for building Gaussian process models for computer experiments with both qualitative and quantitative factors. This approach uses the hypersphere parameterization to model the correlations of the qualitative factors, thus avoiding the need of directly solving optimization problems with positive definite constraints. The effectiveness of the proposed method is successfully illustrated by several examples.

The uncertainty of measurement for various testing operations is affected by quantitative and qualitative factors. The effects of quantitative factors can be estimated using statistical approach, but the same for qualitative factors is difficult to achieve. The measurement of uncertainty in case of various testing processes may be affected by qualitative factors like operator, sample, environmental conditions and machine interference. Various sub-factors related to operator like knowledge, experience, training and attitude may affect the overall performance of test results. The analytical hierarchy process (AHP) is the decision making tool used for ranking of these factors. The objective of paper is to rank these factors using AHP, which is used by the management or administrator to set the strategy for quality improvement by decreasing the uncertainty of measurement.