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This article aims to develop procedures for estimation and prediction in case of Type-I hybrid censored samples drawn from a two-parameter generalized half-logistic distribution (GHLD).

The GHLD is a versatile model which is useful in lifetime modelling. Also, hybrid censoring is a time and cost-effective censoring scheme which is widely used in the literature. The authors derive the maximum likelihood estimates, the maximum product of spacing estimates and Bayes estimates with squared error loss function for the unknown parameters, reliability function and stress-strength reliability. The Bayesian estimation is performed under an informative prior set-up using the “importance sampling technique”. Afterwards, we discuss the Bayesian prediction problem under one and two-sample frameworks and obtain the predictive estimates and intervals with corresponding average interval lengths. Applications of the developed theory are illustrated with the help of two real data sets.

The performances of these estimates and prediction methods are examined under Type-I hybrid censoring scheme with different combinations of sample sizes and time points using Monte Carlo simulation techniques. The simulation results show that the developed estimates are quite satisfactory. Bayes estimates and predictive intervals estimate the reliability characteristics efficiently.

The proposed methodology may be used to estimate future observations when the available data are Type-I hybrid censored. This study would help in estimating and predicting the mission time as well as stress-strength reliability when the data are censored.

We test the proposition that criminal sentiments, which we define as a negative and potent view of a juvenile delinquent (JD), moderate the effect of a delinquency adjudication on self-sentiments. We expect criminal sentiments to reduce self-evaluation and increase self-potency among juvenile delinquents but have no effect on self-sentiments among non-delinquents. We also examine the construct validity of our measure of criminal sentiments by assessing its relationship to beliefs that most people devalue, discriminate against, and fear JDs.

We test these hypotheses with self-administered survey data from two samples of college students and one sample of youths in an aftercare program for delinquent youths. We use endogenous treatment-regression models to identify and reduce the effects of endogeneity between delinquency status and self-sentiments.

Our construct validity assessment shows, as expected, that criminal sentiments are positively related to beliefs that most people devalue, discriminate against, and fear JDs. Our focal analyses support our self-evaluation predictions but not our self-potency predictions.

Our findings suggest that the negative effect of a delinquency label on JDs’ self-esteem depends on the youths’ view of the delinquency label.

This study is the first to test a modified labeling theory proposition on juvenile delinquents.

Several studies have observed that stocks tend to drop by an amount that is less than the dividend on the ex-dividend day, the so-called ex-dividend day anomaly. However, there still remains a lack of consensus for a single explanation of this anomaly. Different from other studies, this dissertation attempts to answer the primary research question: how can investors make trading profits from the ex-dividend day anomaly and how much can they earn? With this goal, I examine the economic motivations of equity investors through four main hypotheses identified in the anomaly's literature: the tax differential hypothesis, the short-term trading hypothesis, the tick size hypothesis, and the leverage hypothesis.

While the U.S. ex-dividend anomaly is well studied, I examine a long data window (1975–2010) of Thailand data. The unique structure of the Thai stock market allows me to assess all four main hypotheses proposed in the literature simultaneously. Although I extract the sample data from two data sources, I demonstrate that the combined data are consistently sampled. I further construct three trading strategies – “daily return,” “lag one daily return,” and “weekly return” – to alleviate the potential effect of irregular data observation.

I find that the ex-dividend day anomaly exists in Thailand, is governed by the tax differential, and is driven by short-term trading activities. That is, investors trade heavily around the ex-dividend day to reap the benefits of the tax differential. I find mixed results for the predictions of the tick size hypothesis and results that are inconsistent with the predictions of the leverage hypothesis.

I conclude that, on the Stock Exchange of Thailand, juristic and foreign investors can profitably buy stocks cum-dividend and sell them ex-dividend while local investors should engage in short sale transactions. On average, investors who employ the daily return strategy have earned significant abnormal return up to 0.15% (45.66% annualized rate) and up to 0.17% (50.99% annualized rate) for the lag one daily return strategy. Investors can also make a trading profit by conducting the weekly return strategy and earn up to 0.59% (35.67% annualized rate), on average.

This study aims to examine the potential effects of the gender similarity between the presenter and evaluator on the presentation evaluation scores obtained with an evaluation form.

The data were collected from marketing students at two universities in the USA. A rubric and separate survey instrument were used to capture student presentation evaluation scores and perceptions of gender differences in various aspects of presentation quality.

Findings indicate that gender of evaluators or presenters did not have any significant effect on presentation scores. The survey of student perceptions of gender effect on student presentations indicate that while female students seem to be perceived as better presenters than male students, the study found no consistent patterns of gender effect on presentation evaluations.

Only four evaluation criteria were used to measure presentation quality.

The results of this exploratory study uses the actual presentation evaluations and survey of student perceptions suggesting that student inputs can be included for grading without any concern of gender bias on grading.

This chapter introduces four research methods that are not covered in the previous chapters. They are (1) non-parametric statistics, (2) interpretive structural modeling, (3) analytic hierarchy process, and (4) data envelopment analysis. The methods are discussed with examples. The discussion, however, is introductory; so we urge the reader to go through the pertinent references for details.

The purpose of this paper is to propose a set of process capability indices (PCIs) which are based on robust and agile statistics such that they may be applicable irrespective of the process status.

The four popular PCIs – Cp, Cpk, Cpm and Cpmk – are reconstructed to improve location and dispersion predictions by introducing robust estimators such as the median and the interquartile range. The proposed PCIs are sequentially evaluated in partitioned regions where fluctuations are inspected to be not significant. The runs test playing the role of a detector permits marking those regions between two consecutive appearances of causes that disrupt data randomness. Wilcoxon's one-sample test is utilized to approximate PCI's central tendency and its confidence interval across all formed partitions.

The Cpmk depicted the most conservative view of the process status when tracking the magnesium content in a showcased aluminum manufacturing paradigm. Cp and Cpk were benchmarked with controlled random data. It was found that the proposed set of robust PCIs are substantially less prone to false alarm in predicting non-conforming units in comparison to the regular PCIs.

The recommended method for estimating PCIs is purely distribution-free and thus deployable at any process maturity level. The advantageous approach defends vigorously against the influence of intruding sources of unknown and unknowable variability. Therefore, the predicament here is to protect the monitoring indicators from unforeseen data instability and breakdown, which are conspicuous in wreaking havoc in managerial decisions.

Addresses the standardization of the measurements and the labels for concepts commonly used in the study of work organizations. As a reference handbook and research tool, seeks to improve measurement in the study of work organizations and to facilitate the teaching of introductory courses in this subject. Focuses solely on work organizations, that is, social systems in which members work for money. Defines measurement and distinguishes four levels: nominal, ordinal, interval and ratio. Selects specific measures on the basis of quality, diversity, simplicity and availability and evaluates each measure for its validity and reliability. Employs a set of 38 concepts ‐ ranging from “absenteeism” to “turnover” as the handbook’s frame of reference. Concludes by reviewing organizational measurement over the past 30 years and recommending future measurement reseach.

The fused filament fabrication (FFF) process is an additive manufacturing technique used in engineering design. The mechanical properties of parts manufactured by FFF are influenced by the printing parameters. The mechanical properties of rigid thermoplastics for FFF are well defined, while thermoplastic elastomers (TPE) are uncommonly investigated. The purpose of this paper is to investigate the influence of extruder temperature, bed temperature and printing speed on the mechanical properties of a thermoplastic elastomer.

Regression models predicting mechanical properties as a function of extruder temperature, bed temperature and printing speed were developed. Tensile specimens were tested according to ASTM D638. A 3×3 full factorial analysis, consisting of 81 experiments and 27 printing conditions was performed, and models were developed in Minitab. Tensile tests verifying the models were conducted at two selected printing conditions to assess predictive capability.

Each mechanical property was significantly affected by at least two of the investigated FFF parameters, where printing speed and extruder temperature terms influenced all mechanical properties (p < 0.05). Notably, tensile modulus could be increased by 21%, from 200 to 244 MPa. Verification prints exhibited properties within 10% of the predictions. Not all properties could be maximized together, emphasizing the importance of understanding FFF parameter effects on mechanical properties when making design decisions.

This work developed a model to assess FFF parameter influence on mechanical properties of a previously unstudied thermoplastic elastomer and made property predictions within 10% accuracy.

The purpose of study is to overcome the error estimation of standard deviation derived from Expected improvement (EI) criterion. Compared with other popular methods, a quantitative model assessment and analysis tool, termed high-dimensional model representation (HDMR), is suggested to be integrated with an EI-assisted sampling strategy.

To predict standard deviation directly, Kriging is imported. Furthermore, to compensate for the underestimation of error in the Kriging predictor, a Pareto frontier (PF)-EI (PFEI) criterion is also suggested. Compared with other surrogate-assisted optimization methods, the distinctive characteristic of HDMR is to disclose the correlations among component functions. If only low correlation terms are considered, the number of function evaluations for HDMR grows only polynomially with the number of input variables and correlative terms.

To validate the suggested method, various nonlinear and high-dimensional mathematical functions are tested. The results show the suggested method is potential for solving complicated real engineering problems.

In this study, the authors hope to integrate superiorities of PFEI and HDMR to improve optimization performance.

By using small reference samples, the calculation method of confidence value and prediction method of confidence interval for multi-input system are investigated. The purpose of this paper is to offer effective assessing methods of confidence value and confidence interval for the simulation models used in establishing guidance and control systems.

In this paper, first, an improved cluster estimation method is proposed to guide the selection of the small reference samples. Then, based on analytic hierarchy process method, the new calculation method of the weight of each reference sample is derived. By using the grey relation analysis method, new calculation methods of the correlation coefficient and confidence value are presented. Moreover, the confidence interval of the sample awaiting assessment is defined. A new prediction method is derived to obtain the confidence interval of the sample awaiting assessment which has no reference sample. Subsequently, by using the prediction method and original small reference samples, Bootstrap resampling method is used to obtain more correlation coefficients for the sample to reduce the probability of abandoning the true.

The grey relational analysis is used in assessing the confidence value and interval prediction. The numerical simulations are presented to demonstrate the effectiveness of the theoretical results.

Based on the selected small reference samples, new calculation methods of the correlation coefficient and confidence value are presented to assess the confidence value of model awaiting assessment. The calculation methods of maximum confidence interval, expected confidence interval and other required confidence intervals are presented, which can be used in assessing the validities of controller and guidance system obtained from the model awaiting assessment.