Understanding prediction intervals for firm specific inefficiency scores from parametric stochastic frontier models

We examine the effect of varying the temperature points on MEMS inertial sensors’ noise models using Allan variance and least-squares spectral analysis (LSSA). Allan variance is a method of representing root-mean-square random drift error as a function of averaging times. LSSA is an alternative to the classical Fourier methods and has been applied successfully by a number of researchers in the study of the noise characteristics of experimental series. Static data sets are collected at different temperature points using two MEMS-based IMUs, namely MotionPakII and Crossbow AHRS300CC. The performance of the two MEMS inertial sensors is predicted from the Allan variance estimation results at different temperature points and the LSSA is used to study the noise characteristics and define the sensors’ stochastic model parameters. It is shown that the stochastic characteristics of MEMS-based inertial sensors can be identified using Allan variance estimation and LSSA and the sensors’ stochastic model parameters are temperature dependent. Also, the Kaiser window FIR low-pass filter is used to investigate the effect of de-noising stage on the stochastic model. It is shown that the stochastic model is also dependent on the chosen cut-off frequency.

The Fundamentals of Survival and Event History Analysis Introduction: What Is Survival and Event History Analysis? Key Concepts and Terminology Censoring and Truncation Mathematical Expression and Relation of Basic Statistical Functions How Do the Survivor, Density and Hazard Function Relate? Why Use Survival and Event History Analysis? Overview of Survival and Event History Models Exercises Using R and Other Computer Programs for Survival and Event History Analysis Introduction: Computer Programs for Survival and Event History Analysis Conducting Serious Data Analysis: Life Lessons Why Use R? Downloading R on Your Personal Computer Add-On Packages Running R Determining and Setting your Working Directory Help and Documentation Importing Data Into R Working With Data: Opening and Accessing Variables from a Data Frame Saving Output as File, Workspace and History and Quitting R Exercises Your First Session: Using the Survival Package and Exploring Data Via Descriptive Statistics and Graphs Your First Session Using the 'Survival' Package In F Loading and Examining the Survival Package and Rcmdrplugin.Survival Plug-In Opening and Examining Data The Surv Object: Packaging the 'Survival Variable' Basic Descriptive Statistics Descriptive Data Exploration with Graphs Exercises Data and Data Reconstruction Introduction: Why Discuss Data and Data Preparation? Sources of Event History Data Single-Episode Data for Single Transition Analyses Multi-Episode Data for Recurrent Event and Frailty Analyses Subject-(Person)-Period Data for Discrete-Time Hazard Models The Counting Process and Episode Splitting A Note on Dates Exercises Non-Parametric Methods: Estimating and Comparing Survival Curves Using the Kaplan-Meier Estimator Introduction The Kaplan-Meier Estimator Producing Kaplan-Meier Estimates Plotting the Kaplan-Meier Survival Curve Testing Differences Between Two Groups Using Survdiff Stratifying the Analysis by a Covariate Exercises The Cox Proportional-Hazards Regression Introduction: Why is The Cox Model So Popular? The Cox Regression Model Estimating and Interpreting The Cox Model with Fixed Covariates The Cox Regression Model with Time-Varying Covariates Exercises Parametric Models Introduction: What are Parametric Models and Why Use Them? Proportional Hazards (Ph) Versus Accelerated Failure Time (Aft) Models The Path to Choosing a Model Estimating and Interpreting Parametric Survival Models Exponential and Piecewise Constant Exponential Model Weibull Model Log-Logistic and Log-Normal Models Additional Parametric Models Finding the Best Fitting Parametric Model Exercises Model Building and Diagnostics Introduction Model Building and Selection of Covariates Assessing the Overall Goodness of Fit of Your Model What is Residual Analysis? Testing Overall Model Adequacy: Cox-Snell Residuals Testing the Proportional Hazards Assumption: Schoenfeld Residuals Checking For Influential Observations: Score Residuals (Dfbeta Statistics) Assessing Nonlinearity: Martingale Residual and Component-Plus-Residual Plots Exercises Correlated and Discrete-Time Survival Data: Frailty, Recurrent Events and Discrete-Time Models Introduction Shared Frailty: Modeling Recurrent Events and Clustering In Groups Other Frailty Models: Unshared, Nested, Joint and Additive Models Estimating Frailty Models in R Example of Frailty Model Estimation and Interpretation Discrete-Time and Count Models Exercises Multiple Events and Entire Histories: Competing Risk, Multistate Models and Sequence Analysis Introduction Competing Risk Models Multistate Models Sequence Analysis: Modeling Entire Histories Exercises Appendix : Datasets Used in this Book

Estimation of Jump-Diffusion Processes via Empirical Characteristic Functions

As a hydrologic study finds answers to problems, the learning experience builds human capital. This “learning by doing” is illustrated in studies of flood risks below Utah canyons. Aversion of the Stanford Watershed Model (SWM) with five principal parameters was applied to several heterogeneous catchments. The Wasatch Front rises 1600 meters above the Great Salt Lake and is drained by many parallel mountain canyons whose side slopes average about 35 percent. Since most precipitation is snow, the modeling covered snow accumulation, solar radiation, melt runoff, soil moisture, landslides, debris flows, routing, and sediment transport. The poor explanation of certain physical processes suggested use of a stochastic parametric model that can be segmented into progressively smaller units to find a scale of best fit. Critical thinking can then examine the identified problem conditions with a physical model at that scale.

A stochastic modelling approach for the characterisation of collision exchange processes

Stochastic models specified by statistical source parameters have been proposed to describe the details of fault plane irregularities. The statisical parameters have been estimated through the analysis of strong-motion records by using these models. In the study herein, a new semi-empirical method is developed to estimate strong ground-motions in the short period range, taking into account the fault plane irregularities.The fault plane of large earthquake is assumed to consist of some small segments with a specific size, which is called fault patch size. The displacement Dij of each segment is divided into two parts. One is the average displacement D0 over the fault plane and the other is the deviation ΔDij from D0. Seismic wave SL(t) due to D0 and the seismic wave SS(t) due to ΔDij are synthesized separately as follows:SL(t)=nΣi=1nΣj=1nΣk=1(Re/Rij)Se(t-τijk)SS(t)=nΣi=1nΣj=1κij(Re/Rij)Se(t-τij1)where Se(t) shows an observed seismic wave of element earthquake, of which fault area should be consistent with the fault patch size, Re is the source to site distance of the element earthquake, Rij is a distance from each segment on the fault plane to the site, and τijk is a delay time in relation to rupture propagation and travel time of seismic waves.SL(t) can be derived based on the similarity conditions of source parameters and n is determined from the cubic root of the seismic-moment ratio between the large and the element earthquakes. κij is introduced as a ratio between the deviation ΔDij of each segment on the fault plane and the displacement of element earthquake, and is assumed to be a probabiristic parameter of normal distribution with average of 0 and with standard deviation of SD. SD shows the degree of inhomogeneity of displacement on the fault plane. The final result of synthesized strong ground-motion can be expressed as a sum of SL(t)and SS(t).The new method is applied to synthesis of strong ground motions at Akita during the 1983 Nihonkai-chubu earthquake (M=7.7) and the relations among SD and some statistical parameters of other stochastic fault models are discussed theoretically.

The penalized simulated maximum likelihood (PSML) approach can be used to estimate parameters for a stochastic differential equation model based on completely or partially observed discrete-time observations. The PSML uses an auxiliary variable importance sampler and parameters are estimated in a penalized maximum likelihood framework. In this paper, we extend the PSML to allow for measurement error, including unknown initial conditions. Simulation studies for two stochastic models and a real world example aimed at understanding the dynamics of chronic wasting disease illustrate that our method has favorable performance in the presence of measurement error. PSML reduces both the bias and root mean squared error as compared to existing methods. Lastly, we establish consistency and asymptotic normality for the proposed estimators.

Wastewater production, like many other engineered and environmental processes, is inherent stochastic in nature and requires the use of complex stochastic models, for example, to predict realistic patterns of down-the-drain chemicals or pharmaceuticals and personal care products. Up until now, a formal method of statistical inference has been lacking for many of those models, where explicit likelihood functions were intractable. In this Article, we investigate Approximate Bayesian Computation (ABC) methods to infer important parameters of stochastic environmental models. ABC methods have been recently suggested to perform model-based inference in a Bayesian setting when model likelihoods are analytically or computationally intractable and have not been applied to environmental systems analysis or water quality modeling before. In a case study, we investigate the performance of three different algorithms to infer the number of wastewater pulses contained in three high-resolution data series of benzotriazole and total nitrogen loads in sewers. We find that all algorithms perform well and that the uncertainty in the inferred number of corresponding wastewater pulses varies between 6% and 28%. In our case, the results are more sensitive to substance characteristics than to catchment properties. Although the application of ABC methods requires careful tuning and attention to detail, they have a great general potential to update stochastic model parameters with monitoring data and improve their predictive capabilities.

Chained and tandem schedules are a common method for thinning schedules of reinforcement following functional communication training (FCT) in the treatment of problem behavior. We conducted a systematic literature review and meta-analysis of chained and tandem schedules following FCT to describe treatment characteristics and evaluate effects. We identified 38 articles and found reductions in problem behavior across four measures. Results of a random-effects multilevel meta-analysis of rigorous evaluations showed a significant effect of chained or tandem schedules + FCT on problem behavior relative to baseline. We observed resurgence in 77% of cases and 31% of schedule-thinning transitions. Results showed chained schedules resulted in greater reductions in problem behavior relative to tandem schedules. Exploratory moderator analyses suggested extinction, combined reinforcement, and delay and denial tolerance training procedures were associated with greater reductions in problem behavior relative to other treatment characteristics. Implications for practice and future research are discussed.

Fourier series are used to describe the seasonal variation of the five parameters for a stochastic model of daily precipitation utilizing a first‐order Markov chain for the occurrence process and a mixed exponential distribution for the daily precipitation amounts (MCME model). Spatial variability of the means of each parameter for 16 stations in South Dakota has been illustrated by mapping isopleths. MCME parameters for 4 stations not included in the analysis are more closely described by the arithmetic mean of parameters for the 6 nearest stations than by using parameters for the nearest neighboring station or parameters estimated by spline fitting or linear interpolation. However, MCME parameters estimated by all interpolation methods were significantly different from parameters identified for each of the four stations by maximum likelihood techniques. The principal source of this spatial variability at distances of on the order of 100 km is data inconsistency due to methodological differences affecting small precipitation amounts and apparently related to observation time. Sampling error, possible parameter identifiability problems, and real differences in the precipitation regime on a scale smaller than the station spacing also contribute to the observed variability.

In parametric stochastic frontier models, the composed error is specified as the sum of a two-sided noise component and a one-sided inefficiency component, which is usually assumed to be half-normal, implying that the error distribution is skewed in one direction. In practice, however, estimation residuals may display skewness in the wrong direction. Model respecification or pulling a new sample is often prescribed. Since wrong skewness may manifest as a finite sample problem, this paper proposes a finite sample adjustment to existing estimators to obtain the desired direction of residual skewness. This provides an alternative empirical approach to deal with the wrong skewness problem that does not require respecification of the model.

Short-term rental accommodation is one of the biggest challenges facing the hotel sector. Despite the prevalence of short-term rental accommodation and its growing importance in the lodging industry, its effects on the efficiency of the hotel sector remain unexplored. This study examines the impact of short-term rental accommodation on regional hotel efficiency using a parametric stochastic frontier model. The analysis is based on data relating to Spain, a key destination in the global tourism sector. The study employs monthly panel data for the period 2008–2019 for hotels located in each of the 17 Spanish regions. The results show that hotels have low levels of technical efficiency, although all regions generally improved their performance over time. Interestingly, a higher number of short-term rentals is associated with greater technical efficiency of hotels. This finding suggests that each form of accommodation complements the other.

A Monte Carlo Study of Efficiency Estimates from Frontier Models

English is one of the most used languages for jobs, markets, tourism, discourse and international connectivity. However, English learners face many challenges in gaining English language skills. Extant studies show that AI has affordances to support in English language teaching and learning ELT/L. This study answers the call to examine specific challenges and affordances for using AI in ELT/L. A systematic review method was used with PRISMA principles to identify 42 studies. Findings reveal the geographical locations of studies, learner ages and years of study. Grounded coding was then used to identify affordances of the use of AI in ELT/L in the areas of speaking, writing, reading, pedagogy and self‐regulation. AI in ELT/L challenges uncovered were technology breakdowns, limited capabilities, fear and standardising language. Policymakers, funders, practitioners and educational leaders can use the information provided in this study to gain a holistic understanding of the current trend in the use of AI in ELT/L, and practical implications are provided to guide future use of AI. Practitioner notes What is already known about this topic English is one of the most used languages for jobs, markets, tourism, discourse and international connectivity. Empirical evidence shows that pupils can often face difficulties when learning English, with challenges such as irregularity in English spelling. AI has supported language teaching and learning with studies showing that AI can support language‐specific skills. What this paper adds Provides the scholarly community with a unique systematic review in the use of AI in ELT/L across learner levels. Identifies affordances of AI in ELT/L in speaking, writing, reading, pedagogy and self‐regulation. Identifies challenges of AI in ELT/L in technology breakdowns, limited capabilities, fear and standardising language. Provides researchers with a review of the field with identification of gaps and future research opportunities. Implications for practice and/or policy Provides practical implications from the findings for educators, policy makers and program designers. Highlights the gaps in academic knowledge as a lack in the use of AI for assessment in ELT /L.

I provide conditions under which the trimmed FDQML estimator, advanced by McCloskey (2010) in the context of fully parametric short‐memory models, can be used to estimate the long‐memory stochastic volatility model parameters in the presence of additive low‐frequency contamination in log‐squared returns. The types of low‐frequency contamination covered include level shifts as well as deterministic trends. I establish consistency and asymptotic normality in the presence or absence of such low‐frequency contamination under certain conditions on the growth rate of the trimming parameter. I also provide theoretical guidance on the choice of trimming parameter by heuristically obtaining its asymptotic MSE‐optimal rate under certain types of low‐frequency contamination. A simulation study examines the finite sample properties of the robust estimator, showing substantial gains from its use in the presence of level shifts. The finite sample analysis also explores how different levels of trimming affect the parameter estimates in the presence and absence of low‐frequency contamination and long‐memory.