Estimation Procedure Research Articles

Assessing heterogeneity in surrogacy using censored data.

Determining whether a surrogate marker can be used to replace a primary outcome in a clinical study is complex. While many statistical methods have been developed to formally evaluate a surrogate marker, they generally do not provide a way to examine heterogeneity in the utility of a surrogate marker. Similar to treatment effect heterogeneity, where the effect of a treatment varies based on a patient characteristic, heterogeneity in surrogacy means that the strength or utility of the surrogate marker varies based on a patient characteristic. The few methods that have been recently developed to examine such heterogeneity cannot accommodate censored data. Studies with a censored outcome are typically the studies that could most benefit from a surrogate because the follow-up time is often long. In this paper, we develop a robust nonparametric approach to assess heterogeneity in the utility of a surrogate marker with respect to a baseline variable in a censored time-to-event outcome setting. In addition, we propose and evaluate a testing procedure to formally test for heterogeneity at a single time point or across multiple time points simultaneously. Finite sample performance of our estimation and testing procedure are examined in a simulation study. We use our proposed method to investigate the complex relationship between change in fasting plasma glucose, diabetes, and sex hormones using data from the diabetes prevention program study.

Valid instrumental variable selection method using negative control outcomes and constructing efficient estimator.

In observational studies, instrumental variable (IV) methods are commonly applied when there are unmeasured covariates. In Mendelian randomization, constructing an allele score using many single nucleotide polymorphisms is often implemented; however, estimating biased causal effects by including some invalid IVs poses some risks. Invalid IVs are those IV candidates that are associated with unobserved variables. To solve this problem, we developed a novel strategy using negative control outcomes (NCOs) as auxiliary variables. Using NCOs, we are able to select only valid IVs and exclude invalid IVs without knowing which of the instruments are invalid. We also developed a new two-step estimation procedure and proved the semiparametric efficiency of our estimator. The performance of our proposed method was superior to some previous methods through simulations. Subsequently, we applied the proposed method to the UK Biobank dataset. Our results demonstrate that the use of an auxiliary variable, such as an NCO, enables the selection of valid IVs with assumptions different from those used in previousmethods.

An Application of a Small Area Procedure with Correlation Between Measurement Error and Sampling Error to the Conservation Effects Assessment Project

County level estimates of mean sheet and rill erosion from the Conservation Effects Assessment Project (CEAP) are useful for program development and evaluation. Since county sample sizes in the CEAP survey are insufficient to support reliable direct estimators, small area estimation procedures are needed. The quantity of water runoff is a useful covariate but is unavailable for the full population. We use an estimate of mean runoff from the CEAP survey as a covariate in a small area model with sheet and rill erosion as the response. As the runoff and sheet and rill erosion are estimators from the same survey, the measurement error in the covariate is important as is the correlation between the measurement error and the sampling error. We conduct a detailed investigation of small area estimation in the presence of a correlation between the measurement error in the covariate and the sampling error in the response. In simulations, the proposed predictor is superior to small area predictors that assume the response and covariate are uncorrelated or that ignore the measurement error entirely.

Predicting distribution of aeolian vibration amplitude of undamped overhead transmission lines

The most widely accepted estimation procedure of the severity of aeolian vibration is by calculating the maximum oscillation amplitudes of the conductor using Energy Balance Principle (EBP). However, the EBP is based on wind tunnel results where only one frequency is excited, while observations and experimental results show that multiple resonant modes are excited simultaneously. Furthermore, the required number of cycles of each amplitude level is not provided by current EBP-based methods. In this paper, vibration data from an experimental undamped ACSR Bersfort test line in Quebec, Canada, is recorded and analyzed. For each record of aeolian vibrations, amplitudes are fitted to a Rayleigh distribution based on the narrow-band assumption. The number of cycles and Rayleigh parameter are then related to wind conditions through a modified Strouhal frequency and EBP methodology. A statistical model is proposed to relate vibration profiles and wind input while considering wind turbulence intensity. The proposed method performs well and gives accurate estimates of both vibration amplitudes and number of cycles for ACSR Bersfort conductor. Physical and statistical theory is provided for each step of the method in order to extend the application of the method to other types of conductors or different line configurations.

Modelling Li-V2O5 Batteries Using Galvanostatic Intermittent Titration Technique and Electrochemical Impedance Spectroscopy: Towards Final Applications

Given the relevance of lithium and post-lithium batteries as electrochemical energy storage systems, the peculiar crystalline structure of V2O5 and its doping capacity play key roles in lithium-ion battery technology. To integrate them in high-efficiency modules, systematic methodologies are required to estimate the state of charge in a reliable way and predict the Li-V2O5 battery’s performance according to their electrochemical phenomena, including two plateaus in the galvanostatic cycling curves and the dynamic behavior governed by the energy demand. Most state of charge estimation and battery modeling procedures are focused on conventional Li-batteries that show a unique plateau. In this work, we propose a systematic methodology based on the galvanostatic intermittent titration technique and electrochemical impedance spectroscopy to study battery performance in the time and frequency domains, respectively. The proposed methodology, with a time–frequency correlation, promotes a deeper understanding of the electrochemical phenomena and general behavior of Li-V2O5 batteries, allowing for its subsequent extrapolation to more complex and higher-capacity lithium and post-lithium batteries used in high-power applications with a minimum error.

Efficient reliability analysis of stochastic dynamic first-passage problems by probability density evolution analysis with subset supported point selection

This study introduces a novel point selection procedure for the Probability Density Evolution Method (PDEM) to estimate time-dependent reliability and failure probabilities in dynamic systems under first-passage failure conditions. The method integrates and modifies features of the Subset simulation procedure to adaptively generate dependent sample sets suitable for a reliability analysis by a direct probability integration approach levering PDEM. Performance function assessments are used as weighting factors in the Subset supported Point Selection (S-PS), enhancing the ultimate failure probability estimation accuracy. The presented approach effectively identifies samples in the failure region, particularly benefiting for dynamic systems under stochastic excitation, tested with random dimensions up to 60. It also offers a computationally efficient structural reliability estimation procedure by analyzing full time–history responses. The proposed method provides deeper insights into rare failure events and mechanisms through the visualization of intermediate results. This research presents an advanced framework for estimating structural reliability and understanding critical events in dynamic systems.

Sequential Data Integration Under Dataset Shift

With the rapidly increasing availability of large-scale and high-velocity streaming data, efficient algorithms that can process data in batches without requiring expensive storage and computation resources have drawn considerable attention. An emerging challenge in developing efficient batch processing techniques is dataset shift, where the joint distribution of the collected data varies across batches. If not recognized and addressed properly, dataset shift often leads to erroneous statistical inferences when integrating data from different batches. In this article, two shift-adjusted estimation procedures are developed for updated estimation of the parameter in the presence of dataset shift. Under prior probability shift, we can obtain parameter estimation and assess the degree of dataset shift simultaneously. We study the asymptotic properties of the proposed estimators and evaluate their performance in numerical studies. The proposed methodologies are illustrated with an analysis of the Ford GoBike docked bike-sharing data.

Persistent trends and inefficiencies in the Greek housing market: a sentiment based approach

PurposeWe examine the aggregate price trend of the Greek housing market from a behavioral perspective.Design/methodology/approachWe construct a behavioral real estate sentiment index, based on relevant real estate search terms from Google Trends and websites, and examine its association with real estate price distributions and trends. By employing EGARCH(1,1) on the New Apartments Index data from the Bank of Greece, we capture real estate price volatility and asymmetric effects resulting from changes in the real estate search index. Enhancing robustness, macroeconomic variables are added to the mean equation. Additionally, a run test assesses the efficiency of the Greek housing market.FindingsThe results show a significant relationship between the Greek housing market and our real estate sentiment index; an increase (decrease) in search activity, indicating a growing interest in the real estate market, is strongly linked to potential increases (decreases) in real estate prices. These results remain robust across various estimation procedures and control variables. These findings underscore the influential role of real estate sentiment on the Greek housing market and highlight the importance of considering behavioral factors when analyzing and predicting trends in the housing market.Originality/valueTo investigate the behavioral effect on the Greek housing market, we construct our behavioral pattern indexes using Google search-based sentiment data from Google Trends. Additionally, we incorporate the Google Trend index as an explanatory variable in the EGARCH mean equation to evaluate the influence of online search behavior on the dynamics and prices of the Greek housing market.

Identification and Estimation of Discrete Choice Models with Unobserved Choice Sets

We propose a framework for nonparametric identification and estimation of discrete choice models with unobserved choice sets. We recover the joint distribution of choice sets and preferences from a cross-section of repeated choices. We assume that either the latent choice sets are sparse or that the number of repeated choices is sufficiently large. Sparsity requires the number of possible choice sets to be relatively small. It is satisfied, for instance, when the choice sets are nested or when they form a partition. Our estimation procedure is computationally fast and uses mixed-integer programming to recover the sparse support of choice sets. Analyzing the ready-to-eat cereal industry using a household scanner dataset, we find that ignoring the unobservability of choice sets can lead to incorrect estimates of preferences.

PSO-PSP-Net + InceptionV3: An optimized hyper-parameter tuned Computer-Aided Diagnostic model for liver tumor detection using CT scan slices

An automated diagnostic system leads to a supreme requirement in medical image analysis, greatly impacting the death rate due to the high spreading rate of liver tumors. However, the traditional histopathological diagnostic processes cause more disruption in the medical detection framework because of the procedure of manual estimation of CT scans and medical labor-intensive, which lead to the time consumption process. It is a highly challenging task for the segmentation of liver tumor because of the irregular appearance, distinct densities, patchy edges, and tumor sizes. To overcome this issue, an automated computer-aided diagnosis framework has been utilized to diagnose the liver cancer quickly and accurately with the less workload of expert radiologists.Presently, Deep learning-based techniques have been applied to identify the tumor regions and assist radiologists in the initial diagnosis and scrutinizing the severity level. The proposed work presents a modified pyramid scene parsing network (PSP-Net) model based on InceptionV3 deep network architecture with a particle swarm optimization algorithm, which increases the efficiency and effectiveness of the liver diagnostic model. Three publicly accessible benchmark datasets have been engaged with the pre-processing task accomplished by image normalization and data augmentation techniques. Moreover, a hyper-parameter tuned improved PSO-PSP-Net algorithm has analyzed the optimal set of gathered features. In the experimental evaluation, the proposed model outperformed than grey wolf optimization (GWO-PSP-Net), PSO-U-Net, GWO-U-Net, opposition based spotted hyena optimization (O-SHO-CRNN), and grey wolf-class topper optimization (GW-CTO) + U-Net by 8.42 %, 4.9 %, 9.54 %, 5.12 %, and 1.75 %, respectively in terms of accuracy for LiTS dataset. The developed PSO-PSP-Net model attained better accuracy than GWO-PSP-Net, PSO-U-Net, GWO-U-Net, and Residual Atrous U-Net (RA-U-Net) for the segmentation of Type-I and Type-II liver tumors by 6.88 %, 8.44 %, 13.75 %, 5.99 % and 6.12 %, 9.74 %, 17.3 %, and 5.11 %, respectively for 3DIRCAD-b1 dataset. The obtained accuracy of the developed model is 14.09 %, 7.54 %, 16.36 %, 2.64 %, and 14.9 %, 8.86 %, 17.76 %, and 4.87 % maximized than GWO-PSP-Net, PSO-U-Net, GWO-U-Net, and RA-U-Net, respectively, for the segmentation of the infected arteries and bones in the 3DIRCAD-b1 dataset. For the CHAOS dataset, the improved PSO-PSP-Net model outperformed GWO-PSP-Net, PSO-U-Net, and GWO-U-Net, respectively, in terms of overall accuracy by 7.44 %, 8.85 %, and 17.2 %, respectively. The investigational analysis validates the proposed PSO-PSP-Net model's effectiveness and performance in obtaining high segmentation accuracy in comparison with other state-of-the-art approaches.

Impact of Trade Liberalisation and MSME Classification on Productivity of Indian Manufacturing Firms

This article examines the effects of tariff and non-tariff reductions on firm-level productivity of large as well as Micro, Small and Medium Enterprises (MSMEs) in India’s manufacturing sector for the 1999–2009 period. We calculate input and final goods tariffs, effective rates of protection and non-tariff barriers (NTBs) for broad product groups using information from India’s Exim Policy of 1997–2003 and 2004–2009 to examine this impact. We use a balanced as well as unbalanced firm-level panel data set, while accounting for firm, industry, state and time-specific factors. By using fixed effect models and Heckman’s two-step estimation procedure, we find that trade liberalisation is associated with improved firm-level productivity for large firms but not in the case of MSMEs. We posit that this might be due to the relative disadvantages that Indian MSMEs face which prevent them from benefiting from trade liberalisation. We also find that productivity gains arising from the sourcing of imported inputs have been greater than those arising from increased product competition and that NTB liberalisation has had a greater impact compared to tariff liberalisation on firm-level productivity. Changes in MSME legislation and the classification of firms are also found to have a bearing on firm performance. Our study contributes to the literature by confirming the need to focus specifically on constraints faced by MSMEs if they are to benefit from trade liberalisation. It highlights the importance of liberalising trade in intermediates as well as the role of MSME classification in enabling productivity gains. JEL Codes: L6, D24, L1, F13

Inference for Compound Exponential XLindley Model with Applications to Lifetime Data

The creating of novel models essentially stems from the requirement to appropriate describe survival cases. In this study, a novel lifetime model with two parameters is proposed and studied for modeling more types of data used in different study cases, including symmetric, asymmetric, skewed, and complex datasets. The proposed model is obtained by compounding the exponential and XLindley distributions, and it is regarded as a strong competitor for the widely applied symmetrical and non-symmetrical models. Several characteristics and statistical properties are investigated. The unknown parameters of the recommended model for the complete sample are estimated using two estimation methods; notably, maximum likelihood estimation and Bayes techniques based on several loss functions as well as an approximate tool are used to construct the confidence intervals for the unknown parameters of the suggested model. The estimation procedures are compared using a Monte Carlo simulation experiment to demonstrate their effectiveness. In the end, the applicability and flexibility of the recommended model are conducted using two real lifetime datasets. In our illustration, we compare the practicality of the recommended model with several well-known competing distributions.

Semiparametric volatility model with varying frequencies

Time series data from various sources usually results to variables measured at varying frequencies as this is often dependent on the source. Modeling from these data can be facilitated by aggregating high frequency data to match the relatively lower frequencies of the rest of the variables. This can easily lose vital information that characterizes the system ought to be modeled. Two semiparametric volatility models are postulated to account for covariates of varying frequencies without aggregation of the data to lower frequencies. First is an extension of the autoregressive integrated moving average with explanatory variable, integrating high frequency data into the mean equation. Second is an extension of the Glosten, Jagannathan, and Rankle model that incorporates the high frequency data into the variance equation. High frequency data were introduced as a nonparametric function in both models that are then estimated using a hybrid estimation procedure that benefits from the additive nature of the models. Simulation studies illustrate the advantages of postulated models in terms of predictive ability compared to GJR models that aggregates high frequency covariates to the same frequency as the output variable. An illustration is provided with stock return data from the Philippines.

Meta-Analysis of Coefficient Alpha: Empirical Demonstration Using English Language Teaching Reflection Inventory

Cronbach’s alpha is a reliability coefficient commonly reported in second language (L2) and English language teaching (ELT) studies. The alpha coefficient provides information on the internal consistency of a measuring instrument. The reported alpha coefficients are obtained from, and apply only to, the research sample. However, the estimation of the alpha coefficient for the population has not received the attention of L2 and ELT researchers. This study aims to provide an overview of the alpha coefficient estimation procedure of a measuring instrument for a population with the reliability generalization method, commonly known as alpha coefficient meta-analysis. An example alpha coefficient meta-analysis study—using empirical data of the 29-item English Language Teaching Reflection Inventory (ELTRI) from 27 independent study samples—was conducted to provide an overview of the procedure for applying the method and the information that needs to be reported from the results of the analysis. The results of the study using a random-effect model show that the population alpha of ELTRI was 0.872, indicating excellent reliability; this is followed by application of a mixed-effect model that shows that article type and means of teaching experience significantly impacted ELTRI reliability. Implications for future research are discussed.

Extreme Treatment Effect: Extrapolating Dose-Response Function into Extreme Treatment Domain

The potential outcomes framework serves as a fundamental tool for quantifying causal effects. The average dose–response function μ(t) (also called the effect curve) is typically of interest when dealing with a continuous treatment variable (exposure). The focus of this work is to determine the impact of an extreme level of treatment, potentially beyond the range of observed values—that is, estimating μ(t) for very large t. Our approach is grounded in the field of statistics known as extreme value theory. We outline key assumptions for the identifiability of the extreme treatment effect. Additionally, we present a novel and consistent estimation procedure that can potentially reduce the dimension of the confounders to at most 3. This is a significant result since typically, the estimation of μ(t) is very challenging due to high-dimensional confounders. In practical applications, our framework proves valuable when assessing the effects of scenarios such as drug overdoses, extreme river discharges, or extremely high temperatures on a variable of interest.

Non-proportional hazards model with a PVF frailty term: application with a melanoma dataset

Survival data analysis often uses the Cox proportional hazards (PH) model. This model is widely applied due to its straightforward interpretation of the hazard ratio under the assumption that the hazard rates for two subjects remain constant over time. However, in several randomized clinical trials with long-term survival data comparing two new treatments, it is frequently observed that Kaplan-Meier plots exhibit crossing survival curves. This violation of the PH assumption of the Cox PH model can not be applied to evaluate the treatment's effect on survival. This paper introduces a novel long-term survival model with non-PH that incorporates a frailty term into the hazard function. This model allows us to examine the effect of prognostic factors on survival and quantify the degree of unobservable heterogeneity. The model parameters are estimated using the maximum likelihood estimation procedure, and we evaluate the performance of the proposed models through simulation studies. Additionally, we demonstrate the applicability of our approach by fitting the models to a real skin cancer dataset.

A simulation study to assess the impact missing values on the performance of different statistical methods for analysis of binary repeated measures data with an additional hierarchical structure

The primary objective of the study was to assess the impact of missing values on the analy- sis of binary repeated measures data with an additional hierarchical structure. One motivat- ing example for the present study was records of high somatic cell counts in milk samples obtained by approximately monthly sampling throughout the lactations of cows in dairy herds. Random effects models with autocorrelated (ρ = 1, 0.9 or 0.5) subject-level ran- dom effects were behind the simulated data. In general, the settings of the simulation were chosen to reflect a real somatic cell count dataset (scc40), except that the within-cow time series length was set to 8-time points for each cow. The estimation procedures consid- ered were: Ordinary Logistic Regression (OLR), Alternating Logistic Regression (ALR), Weighted Generalized Estimating Equations (WGEE), Penalized Quasi Likelihood (PQL), Maximum likelihood via numerical integration (ML) and Bayesian Markov chain Monte Carlo (MCMC). Multiple scenarios of simulated incomplete datasets were considered and include: a scenario corresponded to a combination of missingness patterns present in the scc40 dataset (scc40 scenario) The remaining scenarios involved only drop-outs, and corre- sponded to either moderate or high percentages of values either missing at random (MAR) or not missing at random (NMAR), respectively. In the scc40 scenario, all estimation procedures except OLR performed well and produced estimates with small relative bias (generally less than 5%) for levels of missingness that roughly corresponded to the scc40 data. In MAR missingness scenarios, some biases were found for ALR, WGEE and PQL procedures, whereas the likelihood-based procedures were largely unaffected by the miss- ing values. In NMAR scenarios, all procedures experienced similar and strong biases in the time coefficient; however, fixed effects estimates at the subject and cluster levels were relatively unaffected. Journal of Statistical Research 2023, Vol 57, No.1-2, pp.35-67

Constrained inference in scaled mixed effects models with applications to London Junior School Project data

Multiple outcomes arise frequently in many different settings. Mixed effect models are a useful tool for the analyses of such data. However, when outcomes are measured on different scales, analyses based on any one scale are misleading. Often parameters of the model are subject to known order restrictions. To incorporate heterogeneity across different outcomes, we propose a scaled linear mixed effect model. To estimate parameters, we propose a maximum likelihood estimation procedure based on a restricted version of the expectation-conditional maximization either algorithm. Constrained hypotheses testing procedures are developed using likelihood ratio tests. The empirical significance levels and powers are studied using simulations. This article shows that incorporating the restrictions improves the mean squared errors of the estimates and the power of the tests. The methodology is applied on the London Junior School Project data incorporating known restrictions of patterns of scores. Journal of Statistical Research 2023, Vol 57, No.1-2, pp.81-94

Random Forest Spatial Interpolation Techniques for Crop Yield Estimation at District Level

General Crop Estimation Surveys (GCES) based on Crop Cutting Experiments (CCEs) are conducted for estimation of crop yield following random sampling approach for almost all major crops. About 13 lakh CCEs are conducted every year which has now increased rapidly due to the Pradhan Mantri Fasal Bima Yojana (PMFBY) which is yield based insurance scheme. As suggested by Ministry of Agriculture and Farmers’ Welfare (MoA&FW), this number needs to be reduced drastically by developing sampling procedures based on the use of advanced technologies and advanced survey techniques for crop yield estimation. In this study, an attempt has been made to develop crop yield estimation procedures using Random Forest Spatial Interpolation (RFSI) technique including the spatial variables like spatial distance and nearest neighbours as covariates. RFSI is one of the most adaptable and user-friendly interpolation techniques, as well as one of the fastest in large training datasets. Estimates of yield of wheat were obtained for all the six tehsils of Barabanki district using the estimator under stratified two stage sampling technique. The district level estimates were also obtained by pooling area under wheat crop in each tehsil along with the district level estimate of crop yield, estimate of variance, estimate of standard error (SE) and percentage SE (%SE) of these estimates were also computed in order to make comparison. The results of this study suggest that the estimates derived using RFSI are comparable to kriging and superior to inverse distance weighting (IDW) for the prediction of yield at unknown locations using distance and nearest neighbours.

Hydrologic applicability of satellite-based precipitation estimates for irrigation water management in the data-scarce region

Reliable precipitation estimates are crucial for planning and managing water resources, monitoring hydrologic extremes, and fulfilling irrigation water requirements. Accurate precipitation estimates are particularly challenging in complex mountain terrains, where monitoring gauges are often sparsely distributed due to their remote locations, and high installation and long-term operation costs. Recent advances in satellite-based precipitation estimates offer promising opportunities to improve our understanding of hydrologic processes and their applications for irrigation water management. Several datasets are available varying considerably in terms of their data sources, quality control methods, estimation procedure, and spatiotemporal resolutions. Choosing the most suitable dataset for a particular application is a complex task. In this study, we (1) evaluate the performance of six satellite-based precipitation estimates (SPEs): i) CHIRPS v2.0, ii) CMORPH v1.0, iii) ERA5, iv) IMERG v6, v) MSWEP v2.8, and vi) PERSIANN-CDR against the gauge precipitation using continuous statistical and categorical indices, (2) integrate SPEs with a calibrated semi-distributed hydrologic model to predict streamflow, and (3) demonstrate practical implications of improved streamflow prediction for irrigation water management in the central Himalayan region, Nepal. Our results illustrate that satellite-based precipitation estimates have competitive performance in capturing a wide range of rainfall characteristics, with demonstrated variability across river basins and time scales. There are no significant discrepancies observed in satellite-based precipitation estimates for estimating irrigation water requirements for the three major crops (maize, wheat, and paddy) during the cropping period across the selected river basins, showing a greater promise for irrigation water management planning and decision making.