Replication and methodological robustness in quantitative typology

The response surface method (RSM) is a powerful approach for carrying out reliability analysis for complicate engineering with implicit limit state functions. The quality of a response surface mainly depends on the choice of response surface function and the selection of sample points. To investigate the influence of the types of response surface functions and to reduce the computation efforts, two new sampling methods and a hybrid RSM are proposed. In the hybrid RSM, four types of response surface functions and three sampling methods are involved, and each Response surface function can be connected to each sampling method. The four response surface functions are quadratic polynomial without cross terms (PN1), quadratic polynomial with cross terms, radial basic function network (RBFN) and support vector machine (SVM). The three RSMs using the traditional sampling method, the new iterative sampling method and the new experiment design method are RSM1, RSM2 and RSM3, respectively. The accuracy and efficiency of different RSMs are illustrated through three examples. When an iterative method is used for locating sample points, the PN1-based RSM2 is proposed for its accuracy and efficiency. And when an experiment design method is used for locating sample points, the RBFN- or SVM-based RSM3 is suggested because the RBFN or SVM is suitable for global fitting.

The traditional classical sampling statistics method ignores the spatial location relationship of survey samples, which leads to many problems. This study aimed to propose a spatial sampling method for sampling estimation and optimization of forest biomass, achieving a more efficient and effective monitoring system. In this paper, we used Sequential Gaussian Conditional Simulation (SGCS) to obtain the biomass of four typical forest types in Shangri-La, Yunnan Province, China. In addition, we adopted a geostatistical sampling method for sample point layout and optimization to achieve the purpose of improving sampling efficiency and accuracy, and compared with the traditional sampling method. The main results showed that (1) the Gaussian model, exponential model, and spherical model were used to analyze the variogram of the four typical forests biomass, among which the exponential model had the best fitting effect (R2 = 0.571, RSS = 0.019). The range of the exponential model was 8700 m, and the nugget coefficient (C0/(C0 + C)) was 11.67%, which showed that the exponential model could be used to analyze the variogram of forest biomass. (2) The coefficient of variation (CV) based on 323 biomass field plots was 0.706, and the CV based on SGCS was 0.366. In addition, the Overall Estimate Consistency (OEC) of the simulation result was 0.871, which can be used for comparative analysis of traditional and spatial sampling. (3) Based on the result of SGCS, with 95% reliability, the sample size of traditional equidistant sampling (ES) was 191, and the sampling accuracy was 95.16%. But, the spatial sampling method based on the variation scale needed 92 samples, and the sampling accuracy was 93.12%. On the premise of satisfying sampling accuracy, spatial sampling efficiency was better than traditional ES. (4) The accuracy of stratified sampling (SS) of four typical forest areas based on 191 samples was 97.46%. However, the sampling accuracy of the biomass variance stratified space based on the SGCS was 93.89%, and the sample size was 52. Under the premise of satisfying the sampling accuracy, the sampling efficiency was obviously better than the traditional SS. Therefore, we can obtain the conclusion that the spatial sampling method is superior to the traditional sampling method, as it can reduce sampling costs and solve the problem of sample redundancy in traditional sampling, improving the sampling efficiency and accuracy, which can be used for sampling estimation of forest biomass.

To improve learning performance of Boosting method, Boosting learning procedure is divided into two sequential stages which are named reducing fitting error stage and reducing variance stage according to the idea of generalization error of Boosting method composed of bias and variance which was originally proposed by Breiman. Traditional sampling methods such as roulette wheel selection is suitable for the learning procedure of reducing fitting error stage, and based on the characteristics of reducing variance stage, a new sampling method is proposed named SS method. Based on CSP and SS sampling methods, a new two stages based adaptive sampling Boosting method named ASSBoosting is proposed, which according to the different characteristics of the two learning stages adaptively adopts sampling methods by the comparison of the prediction error caused by the two methods. The results of simulation confirm these findings.

Least squares polynomial chaos expansion: A review of sampling strategies

Summary Environmental DNA (eDNA) sampling can be a highly sensitive method for detecting aquatic taxa; however, the cost‐efficiency of this technique relative to traditional methods has not been rigorously assessed. We show how methods that account for imperfect and stochastic detection can be used to (i) determine the optimal allocation of survey effort with eDNA sampling for a fixed budget (i.e. identify the optimal combination of water samples vs. site visits), and (ii) assess the cost‐efficiency of eDNA sampling relative to traditional survey techniques. We illustrate this approach by comparing eDNA sampling and bottle‐trapping for an exotic newt species (Lissotriton v. vulgaris) recently detected in Melbourne, Australia. Bottle traps produced much lower detection rates than eDNA sampling, but the cost‐efficiency of the two methods can be similar because bottle‐trapping is cheaper per sample. The relative cost‐efficiency of the two sampling methods was sensitive to the available survey budget, the costs of eDNA primer/probe development and sample processing and the number of positive quantitative PCR assays (qPCRs) used to designate a water sample as positive for newt DNA. Environmental DNA sampling was more cost‐efficient than bottle‐trapping for small to intermediate budgets when primer/probe development and sample processing costs were low, and 1/4 or 2/4 positive qPCRs were used to label a water sample as positive for newt eDNA. However, bottle traps were generally more cost‐efficient than eDNA sampling when primer/probe development and sample processing costs were high, regardless of qPCR threshold or survey budget. Traditional sampling methods may achieve lower detection probabilities compared to eDNA sampling, but the totality of costs can make eDNA sampling less efficient than traditional techniques in some circumstances. Our approach provides a quantitative framework for determining how many water samples and site visits are required to maximize detection probabilities with eDNA sampling, and can calculate the cost‐efficiency of any sampling method.

Objectives In order to improve the quality of sinusoidal waveform output by the ship's high-frequency inverter charging device and reduce the CPU occupancy rate, an asymmetric regular sampling sinusoidal pulse width modulation (SPWM) method based on tangent approximation is proposed. Methods According to the basic principle and calculation method of the asymmetric regular sampling SPWM method based on tangent approximation, a Matlab/Simulink simulation model is built, and then the software algorithm flow that can be used in the high-frequency inverter charging device is designed together with the actual output of the tangent approximation method. The effects are then compared and verified by experiments. Results The simulation results show that under pure resistive load and resistive inductive load, the total harmonic distortion (THD) of the load-end waveform based on tangent approximation method is 2.12% and 2.08%, respectively, and its waveform quality is better than symmetric regular sampling. The experimental results show that the THD of load-end waveform based on the tangent approximation method is significantly lower than the symmetric regular sampling method. When the effective value of the input line voltage is 580 V (the modulation ratio is 0.8), the quality of the output waveform is relatively optimal. Conclusions The asymmetric regular sampling SPWM method based on tangent approximation overcomes the shortcomings of the traditional symmetric regular sampling method, such as the low quality of the output waveform and the traditional asymmetric regular sampling method's high sampling frequency and high CPU occupancy rate. The research results can provide reference for the design of ship's high frequency inverter charging devices.

Two main classes of theory have been advanced to explain correlations between linguistic features like those observed by Greenberg (1963). arbitrary constraint theories argue that certain sets of features patterm together because they have a single underlying cause in the innate language faculty (e.g., the Principles and Parameters program; see Chomsky & Lasnik 1993). functional theories argue that languages are less likely to have certain combinations of properties because, although possible in principle, they are harder to learn or to process, or less suitable for efficient communication (Hockett 1960, Bates & MacWhinney 1989, Hawkins 2004, Dryer 2007, Christiansen & Chater 2008; for further discussion see Hawkins 2007 and Jaeger & Tily 2011). The failure of Dunn, Greenhill, Levinson & Gray (2011) to find systematic feature correlations using their novel computational phylogenetic methods calls into question both of these classes of theory. Dunn et al.’s methodology is a principled and powerful new way of evaluating change-based theories of language typology, but it faces fundamental challenges common throughout quantitative typology. Typological data is usually sparse, and additional data hard or impossible to obtain. The statistical power to detect an effect (e.g., to detect universal tendencies) is reduced by uncertainty about the genealogical relations between languages, the period of time during which a language was spoken, and the amount of contact with other languages. It is hence possible that the failure to find support for word order universals reported in Dunn et al. 2011 is a spurious null effect (see Croft et al. 2011, in this issue, who also discuss other potential methodological issues with that study). To some extent, this is a critique that can be leveled at much work on quantitative typology: sparse and uncertain data weakens the conclusions that can be drawn from it. If we desire a higher level of certainty when evaluatAUTHOR’S COPY | AUTORENEXEMPLAR

Determining the species compositions of local assemblages is a prerequisite to understanding how anthropogenic disturbances affect biodiversity. However, biodiversity measurements often remain incomplete due to the limited efficiency of sampling methods. This is particularly true in freshwater tropical environments that host rich fish assemblages, for which assessments are uncertain and often rely on destructive methods. Developing an efficient and nondestructive method to assess biodiversity in tropical freshwaters is highly important. In this study, we tested the efficiency of environmental DNA (eDNA) metabarcoding to assess the fish diversity of 39 Guianese sites. We compared the diversity and composition of assemblages obtained using traditional and metabarcoding methods. More than 7,000 individual fish belonging to 203 Guianese fish species were collected by traditional sampling methods, and ~17 million reads were produced by metabarcoding, among which ~8 million reads were assigned to 148 fish taxonomic units, including 132 fish species. The two methods detected a similar number of species at each site, but the species identities partially matched. The assemblage compositions from the different drainage basins were better discriminated using metabarcoding, revealing that while traditional methods provide a more complete but spatially limited inventory of fish assemblages, metabarcoding provides a more partial but spatially extensive inventory. eDNA metabarcoding can therefore be used for rapid and large-scale biodiversity assessments, while at a local scale, the two approaches are complementary and enable an understanding of realistic fish biodiversity.

This study explores the evaluation of sampling techniques in audit engagements, aiming to provide insights into the factors influencing auditors' decisions and the effectiveness of different sampling methods. The research adopts a qualitative approach, conducting a comprehensive literature review to identify key themes and discussions surrounding sampling techniques in auditing. Factors such as population characteristics, audit objectives, resource constraints, audit complexity, regulatory requirements, and technological advancements are analyzed to understand their impact on sampling decisions. Through thematic analysis, the study identifies emerging themes, including the effectiveness of sampling techniques, factors influencing sampling decisions, and the role of technological advancements in audit sampling. Findings suggest that while traditional sampling methods like random sampling and systematic sampling remain prevalent, innovative approaches such as statistical sampling and probability-proportional-to-size sampling offer enhanced precision and reliability, particularly in complex audit environments. The integration of technology, particularly data analytics tools and audit software, has revolutionized audit sampling practices, enabling auditors to conduct more comprehensive and efficient sampling procedures. Overall, the study contributes to the existing body of knowledge on sampling techniques in auditing and provides practical implications for audit practitioners, researchers, and policymakers.

Michigan water quality standards for public bathing beaches require local health departments to collect and analyze a minimum of three water samples for Escherichia coli during each sampling event. The geometric mean number of E. coli colonies is then compared to the 300 colonies per 100 ml standard to determine compliance. This article compares the results of the currently mandated procedure to a composite sampling method, whereby the three samples are mixed in equal volumes and analyzed once. This effectively replaces the geometric mean of the individual sample results with an arithmetic mean. Although arithmetic means are more affected by outliers, this sensitivity to high concentrations is more health conservative than the geometric mean. During the 2007 sampling season, nine bathing beaches were monitored once each week. Three individual point samples and a composite sample were analyzed for each sampling event. No statistically significant differences in bacteria concentrations were found between composite sample analysis and the arithmetic mean of individual point sample analyses. No violations were detected in the 2007 sampling season, so using historical data, a retrospective analysis was performed on samples gathered at nine bathing beaches in Kalamazoo County, Michigan during the years 2001-2007. The arithmetic mean of the three samples taken at each site served as a surrogate composite sample. The benefits of compositing the three samples were investigated assuming a 2/3 reduction in analytical costs. In the traditional sampling method, three individual samples were obtained and analyzed once in every 3-week period during the summer season, whereas compositing was simulated by taking the arithmetic mean of each week's results. The results of this retrospective cost analysis indicates that ten to 14 violations would have been missed using the less frequent traditional sampling and analysis methodology. Composite sampling is a cost-saving alternative to traditional sampling techniques that can be more protective of public health, particularly when the savings are applied to increased numbers of samples in time or space.

Software defect prediction has gained considerable attention in recent years. A broad range of computational methods has been developed for accurate prediction of faulty modules based on code and design metrics. One of the challenges in training classifiers is the highly imbalanced class distribution in available datasets, leading to an undesirable bias in the prediction performance for the minority class. Data sampling is a widespread technique to tackle this problem. However, traditional sampling methods, which depend mainly on random resampling from a given dataset, do not take advantage of useful information available in training sets, such as sample quality and representative instances. To cope with this limitation, evolutionary undersampling methods are usually used for identifying an optimal sample subset for the training dataset. This paper proposes a binary teaching–learning- based optimization algorithm employing a distribution-based solution update rule, namely BTLBOd, to generate a balanced subset of highly valuable examples. This subset is then applied to train a classifier for reliable prediction of potentially defective modules in a software system. Each individual in BTLBOd includes two vectors: a real-valued vector generated by the distribution-based update mechanism, and a binary vector produced from the corresponding real vector by a proposed mapping function. Empirical results showed that the optimal sample subset produced by BTLBOd might ameliorate the classification accuracy of the predictor on highly imbalanced software defect data. Obtained results also demonstrated the superior performance of the proposed sampling method compared to other popular sampling techniques.

Abstract: This research volume explores the convergence of advanced statistical techniques and qualitative methodologies in the context of modern interdisciplinary research. Rooted in a critical conceptual analysis of epistemological pluralism, the book constructs a robust analytical framework that integrates quantitative rigor with qualitative depth, employing SPSS Amos and NVivo as complementary methodological platforms. The central problem addressed is the fragmentation between statistical modeling and contextual inquiry, which often limits comprehensive understanding in complex research environments such as education, health sciences, and social behavior. The book systematically articulates a methodological response by demonstrating how structural equation modeling, confirmatory factor analysis, and latent variable modeling can be unified with thematic coding, grounded theory, and narrative interpretation. Through detailed case studies, mixed-methods research designs, and data synchronization strategies, the work illustrates how SPSS Amos and NVivo can be employed in tandem to enhance triangulation, validation, and theory development. Key findings highlight the practical viability of integrating these tools to produce empirically robust and contextually meaningful insights. The proposed unified framework addresses both methodological coherence and operational challenges, offering replicable templates and model specifications. This volume contributes significantly to the discourse on methodological innovation, serving as a critical resource for doctoral researchers, applied scientists, and interdisciplinary scholars seeking to navigate the complexities of modern empirical inquiry. Keywords Mixed methods research, SPSS Amos, NVivo, quantitative analysis, qualitative analysis, structural equation modeling, confirmatory factor analysis, thematic coding, data triangulation, epistemological pluralism, validation, research methodology, model integration, grounded theory, interdisciplinary research

Deep learning has been successfully developed as a complicated learning process from source inputs to target outputs in presence of multimedia environments. The inference or optimization is performed over an assumed deterministic model with deep structure. A wide range of temporal and spatial data in language and vision are treated as the inputs or outputs to build such a domain mapping for multimedia applications. A systematic and elaborate transfer is required to meet the mapping between source and target domains. Also, the semantic structure in natural language and computer vision may not be well represented or trained in mathematical logic or computer programs. The distribution function in discrete or continuous latent variable model for words, sentences, images or videos may not be properly decomposed or estimated. The system robustness to heterogeneous environments may not be assured. This tutorial addresses the fundamentals and advances in statistical models and neural networks for domain mapping, and presents a series of deep Bayesian solutions including variational Bayes, sampling method, Bayesian neural network, variational auto-encoder (VAE), stochastic recurrent neural network, sequence-to-sequence model, attention mechanism, end-to-end network, stochastic temporal convolutional network, temporal difference VAE, normalizing flow and neural ordinary differential equation. Enhancing the prior/posterior representation is addressed in different latent variable models. We illustrate how these models are connected and why they work for a variety of applications on complex patterns in language and vision. The word, sentence and image embeddings are merged with semantic constraint or structural information. Bayesian learning is formulated in the optimization procedure where the posterior collapse is tackled. An informative latent space is trained to incorporate deep Bayesian learning in various information systems.

Recently there have been numerous papers, articles and books on the benefits and rewards of Statistical Process Control for manufacturing processes. Models are used that quite adequately describe methods appropriate for the factory situation where many discrete and identical items are turned out and where a limited number of parameters are inspected along the line. Photolithographic applications often require different statistical models from the usual factory methods. The difficulties encountered in getting started with SPC lie in determining: 1. what parameters should be tracked 2. what statistical model is appropriate for each of those parameters 3. how to use the models chosen. This paper describes three statistical models that, among them, account for most operations within a photolithographic manufacturing application. The process of determining which model is appropriate is described, along with the basic rules that may be used in making the determination. In addition, the application of each method is shown, and action instructions are covered. Initially the x-bar, R model is described. This model is the one most often found in off-the-shelf software packages, and enjoys wide applications in equipment tracking, besides general use process control. Secondly the x, moving-R model is described. This is appropriate where a series of measurements of the same parameter is taken on a single item, perhaps at different locations, such as in dimensional uniformity control for wafers or photomasks. In this case, each x is a single observation, or a number of measurements of a single observation, as opposed to a mean value taken in a sampling scheme. Thirdly a model for a Poisson distribution is described, which tends to fit defect density data, particulate counts, where count data is accumulated per unit or per unit time. The purpose of the paper is to briefly describe the included models, for those with little or no background in statistics, to enable them to begin to implement statistical process control and to reap the benefits of a controlled process, prior to, or without investing large amounts of time in training beforehand.

Statistical modeling procedures are presented for representing various conditions in different types of samples and equipment at, or near, the onset of partial discharge (PD). Using multiple-regression techniques it was possible to establish trends in PD behavior in terms of the repetition rate. The latter parameter was chosen as being sensitive to changes near inception, and it is measurable by conventional instrumentation (for example a multichannel pulse-height analyzer). The statistical repetition rate models also can be related to the probability of inception. The probabilistic definition derived complies with the simpler criteria often used in partial-discharge testing. Application of the methods is described by statistical models determined for five examples that include resin-insulated and oil-impregnated systems. Computer analysis showed that a high proportion of the variability of discharge behavior near inception could be estimated from the models: in particular, the effect of voltage in combination with increase in time, insulation thickness, and temperature changes. In addition, the models indicated that characteristic PD patterns are identifiable for resin voids and oil wedges. It is concluded that adoption of statistical techniques could result in the specification of PD inception in terms of probability, thus reducing the difficulties in interpreting the results from commercial tests. >