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Semiparametric models hold promise to address many challenges to statistical inference that arise from real‐world applications, but their novelty and theoretical complexity create challenges for estimation. Taking advantage of the broad applicability of semiparametric models, we propose some novel and improved methods to estimate the regression coefficients of generalized partially linear models (GPLM). This model extends the generalized linear model by adding a nonparametric component. Like in parametric models, variable selection is important in the GPLM to single out the inactive covariates for the response. Instead of deleting inactive covariates, our approach uses them as auxiliary information in the estimation procedure. We then define two models, one that includes all the covariates and another that includes the active covariates only. We then combine these two model estimators optimally to form the pretest and shrinkage estimators. Asymptotic properties are studied to derive the asymptotic biases and risks of the proposed estimators. We show that if the shrinkage dimension exceeds two, the asymptotic risks of the shrinkage estimators are strictly less than those of the full model estimators. Extensive Monte Carlo simulation studies are conducted to examine the finite‐sample performance of the proposed estimation methods. We then apply our proposed methods to two real data sets. Our simulation and real data results show that the proposed estimators perform with higher accuracy and lower variability in the estimation of regression parameters for GPLM compared with competing estimation methods. [ABSTRACT FROM AUTHOR]

Metal silicides and silicates belong to the silicon‐based non‐oxide and oxide ceramics family with exceptional properties. Silicides face fatal oxidation at low temperatures and intrinsic brittleness, whereas silicates face instability in phase at high temperatures which restricts its usage in vast engineering applications. Hence, the ceramic community introduced the concept of high entropy in metal silicides and silicates. Since 2019, high entropy silicides and silicates, a multicomponent system, have created new avenues for materials discovery and design. High entropy silicides displayed elevated properties than the traditional silicides aiming its applications in microelectronic, high‐temperature oxidation resistance coatings, and structural materials. Similarly, high entropy silicates displayed improved properties than the traditional silicates making them the most promising materials for environmental and thermal barrier coating applications for hot section components in gas turbines. The review focuses on specific case studies to emphasize the latest research and developments in high entropy silicides and silicates. Synthesis approaches employed in developing high entropy silicides and silicates and their structural and microstructural outcomes are addressed. The possible application is predicted based on the overview of the properties explored to date. The review concludes with future possibilities offered by the high entropy silicides and silicates. [ABSTRACT FROM AUTHOR]

In this contribution, a pure and robust tungsten oxide (WO3) film was accomplished on stainless steel (SS) substrate at 400°C through a minimal capital intensive, simplistic spray pyrolysis method by utilizing a precursor comprising tungsten hexachloride and 2‐methoxyethanol. Thermal analysis revealed the precursor's thermal decomposition and crystallization at ~230°C and 255°C, respectively. The fabricated polycrystalline (monoclinic crystal structured) film was uniform and dense in nature, exhibiting surface porosity and average surface roughness of 4.7 % and 15.9 nm, respectively. The average grain size and thickness of film were 360 ± 70 nm and ~3.6 μm, respectively. W, O elemental presence with a close atomic ratio of 1:3 on the film's surface was acquired along with 91 % lattice oxygen. Regardless of applied normal load in the range of 5 to 15 N, an increment of ~22 % in scratch hardness was gained in WO3‐coated SS compared to uncoated one. Hydrophilic natured WO3 film (water contact angle, WCA, of ~31°) was efficaciously transformed into hydrophobic (WCA, 136°) by chemical modification with octadecyltrichlorosilane to create a self‐assembled monolayer on the surface of the film. The hydrophobicity of octadecyltrichlorosilane (OTS)‐treated film was found to be preserved even after 100 days. [ABSTRACT FROM AUTHOR]

The effect of Fe3+ ionic substitution in hydroxyapatite (Ca10‐xFex(PO4)6(OH)2) was studied using structural modifications, resulting in an improvement in UV absorption through a tailored optical band structure. Ca2+ of HA being larger compared to Fe3+ contributes to the shrinkage of the lattice. Undoped HA has a peak at 1085 cm−1 (ʋ3 PO43−) which is shifted to 1033 cm−1 for Fe‐HA, because of the perturbation in HA structure. An improvement of UV absorption in the entire UVA and UVB range with an increase in Fe content because of a decrease in bandgap from 5.9 eV to 2.1 eV with undoped and doped HA. Theoretically obtained band gap and optical behaviour of the systems are well correlated with the experimental findings. Moreover, the use of marine biowaste from cuttlefish bone, as the source of HA; low cost and promising UV absorption can have a potential application as UV protective sunscreen filters. [ABSTRACT FROM AUTHOR]

Gamma regression is applied in several areas such as life testing, forecasting cancer incidences, genomics, rainfall prediction, experimental designs, and quality control. Gamma regression models allow for a monotone and no constant hazard in survival models. Owing to the broad applicability of gamma regression, we propose some novel and improved methods to estimate the coefficients of gamma regression model. We combine the unrestricted maximum likelihood (ML) estimators and the estimators that are restricted by linear hypothesis, and we present Stein-type shrinkage estimators (SEs). We then develop an asymptotic theory for SEs and obtain their asymptotic quadratic risks. In addition, we conduct Monte Carlo simulations to study the performance of the estimators in terms of their simulated relative efficiencies. It is evident from our studies that the proposed SEs outperform the usual ML estimators. Furthermore, some tabular and graphical representations are given as proofs of our assertions. This study is finally ended by appraising the performance of our estimators for a real prostate cancer data. [ABSTRACT FROM AUTHOR]

We provide a template for finding target allocation proportions in optimal allocation designs where the target will be invariant for both shifts in location and scale of the response distributions. One possible application of such target allocation proportions is to carry out a response-adaptive allocation. While most of the existing designs are invariant for any change in scale of the underlying distributions, they are not location invariant in most of the cases. First, we indicate this serious flaw in the existing literature and illustrate how this lack of location invariance makes the performance of the designs very poor in terms of allocation for any drastic change in location, such as the changes from degrees centigrade to degrees Fahrenheit. We illustrate that unless a target allocation is location invariant, it might lead to a completely irrelevant and useless target for allocation. Then we discuss how such location invariance can be achieved for general continuous responses. We illustrate the proposed method using some real clinical trial data. We also indicate the possible extension of the procedure for more than two treatments at hand and in the presence of covariates. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]