Aplikasi Akaike Information Criterion (AIC) pada Perhitungan Efisiensi Teknis Perikanan Pukat Cincin di Tuban, Jawa Timur

ABSTRAK Perikanan pukat cincin di Tuban memiliki kontribusi yang tinggi dalam menciptakan variasi hasil tangkapan. Hal ini disebabkan oleh beragamnya metode pengoperasian yang digunakan yang tergambar pada empat faktor produksi yaitu ukuran kapal, jarak daerah penangkapan ikan, jumlah trip dan jumlah anak buah kapal (ABK) yang diambil dengan metode wawancara kepada 60 responden. Metode Akaike Information Criterion (AIC) merupakan metode analisis yang digunakan memperoleh model faktor produksi yang terbaik dengan menggunakan estimasi maximum likelihood sebagai perhitungan yang sesuai. Penelitian ini bertujuan untuk mengetahui model produksi terbaik dengan menggunakan AIC sehingga diperoleh hasil tangkapan yang optimal. Hasil penelitian menunjukkan bahwa jumlah trip merupakan variabel penentu kuantitas hasil tangkapan. Sinkronisasi informasi jumlah trip optimal dan ketentuan upaya penangkapan yang diperbolehkan dapat dijadikan dasar pengelolaan perikanan perikanan pukat cincin di Tuban. Kata Kunci: faktor produksi, ukuran kapal, jarak DPI, jumlah trip, jumlah ABK

An Akaike-type information criterion for model selection under inequality constraints (pre-print)

The Akaike information criterion for model selection presupposes that the parameter space is not subject to order restrictions or inequality constraints. Anraku (1999) proposed a modified version of this criterion, called the order-restricted information criterion, for model selection in the one-way analysis of variance model when the population means are monotonic. We propose a generalization of this to the case when the population means may be restricted by a mixture of linear equality and inequality constraints. If the model has no inequality constraints, then the generalized order-restricted information criterion coincides with the Akaike information criterion. Thus, the former extends the applicability of the latter to model selection in multi-way analysis of variance models when some models may have inequality constraints while others may not. Simulation shows that the information criterion proposed in this paper performs well in selecting the correct model.

Dynamic Model for Biomass and Proteins Production by Three Bacillus Thuringiensis ssp Kurstaki Strains

Bacillus thuringiensis is a microorganism used for the production of biopesticides worldwide. In the present paper, different kinetic models were analyzed to study and compare three different strains of Bt ssp kurstaki (LIP, BLB1, and HD1). Bioperformances (vegetative cell, spore, substrate, and protein) and successive culture phases (oxidative growth, limitation and sporulation, and protein release) were depicted with an overarching aim to estimate total protein productivity, yield, and titer. In the end, two models were calibrated using experimental dataset (11 batches culture in 3 L bioreactor with semisynthetic medium), subsequently validated, and statistically compared. Both models satisfactorily followed the dynamics of the experimental data. Finally, a dynamic model was selected following the Akaike information criterion (AIC).

Evaluación y comparación de algoritmos para la detección automática de eventos sísmicos

La detección temprana de eventos sísmicos permite reducir daños materiales, el número de personas afectadas e incluso salvar vidas. En particular, la actividad sísmica en Ecuador es alta, dado que se encuentra en el denominado Cinturón de Fuego del Pacífico. En tal contexto, el presente artículo tiene como objetivo comparar algoritmos para la detección automática de eventos sísmicos. Dicha comparación se realiza con respecto a la funcionalidad y configuración de los parámetros requeridos para cada algoritmo. Además, la implementación se lleva a cabo sobre una plataforma computacional tipo SBC (Single Board Computer) con la finalidad de obtener una herramienta portable, escalable, económica y de bajo costo computacional. Los métodos comparados son: Classic STA/LTA, Recursive STA/LTA, Delayed STA/LTA, Z-detector, Baer and Kradolfer picker y AR-AIC (Autoregressive-Akaike-Information-Criterion-picker). Para la evaluación y comparación se desarrollan múltiples experimentos empleando registros sísmicos reales proporcionados por la Red Sísmica del Austro (RSA), disponibles como fuente de entrada a los algoritmos. Como resultado se obtiene que el algoritmo Classic STA/LTA presenta el mejor rendimiento, ya que del total de eventos reales (58), solo un evento no fue detectado. Además, se consiguen 6 falsos negativos, logrando un 98,2% de precisión. Cabe recalcar que el software utilizado para la comparación de algoritmos de detección de eventos sísmicos está disponible de forma libre.

SEM-PLS Untuk Analisis Struktur Kemiskinan Pada Masa Pandemi Covid-19

Pandemi Covid-19 menimbulkan berbagai dampak khususnya pada aspek perekonomian. Kondisi perekonomian yang sulit ini menyebabkan pendapatan masyarakat mengalami penurunan, dan menyebabkan jumlah penduduk miskin meningkat. Jumlah penduduk miskin bertambah sebanyak 1,28 juta orang pada tahun 2020. Provinsi Jawa Timur merupakan provinsi yang memiliki tingkat kemiskinan (10,20 persen) sedikit lebih tinggi daripada nasional (10,19 persen) pada tahun 2020. Hal ini dikarenakan adanya dampak pandemi yang menyebabkan hilangnya lapangan pekerjaan dan meningkatnya angka kemiskinan. Penelitian ini akan menganalisis struktur kemiskinan di Provinsi Jawa Timur pada tahun 2020. Tujuan penelitian ini adalah untuk melihat struktur kemiskinan di Provinsi Jawa Timur pada tahun 2020. Metode analisis yang digunakan dalam penelitian ini adalah Structural Equation Modelling (SEM) berbasis komponen yaitu Partial Least Square (PLS). Pada model persamaan struktural terdapat 4 jalur yang signifikan, yaitu pengaruh variabel kesehatan terhadap variabel pendidikan, pengaruh variabel kesehatan dan variabel pendidikan terhadap ekonomi, serta pengaruh variabel ekonomi terhadap variabel kemiskinan. Hasil Analisis Pengelompokan dengan Finite Mixture Partial Least Square berdasarkan kriteria Akaike Information Criterion (AICk), Consistent Akaike Information Criterion (CAICk) dan Bayesian Information Criterion ( BICk) serta Normal Entrophy (EN) diperoleh hasil terbaik yang terbentuk adalah 2 segmen. Sehingga dari 38 kabupaten/kota di wilayah Provinsi Jawa Timur dapat dikelompokkan menjadi 2 segmen. Segmen Pertama sebesar 91,9 persen dari jumlah kabupaten/kota, dan Segmen Kedua sebesar 8,1 persen dari jumlah kabupaten/kota di wilayah Jawa Timur. Kabupaten/kota yang berada pada segmen kedua adalah Kabupaten Situbondo, Kabupaten Nganjuk dan Kota Kediri. Sementara 35 kabupaten/kota lainnya berada di segmen pertama.

Techniques of quality of adjustment of statistical models with evaluation of probability distributions using production data of laying quails

The goal of our study was to evaluate the quality of fit from different types of probability distributions for continuous data. For this, performance traits and quality of quail egg in the production of nutraceutical eggs were used as a continuous data source. The data were collected over 42 days, the experimental design was completely randomized with 7 treatments, 6 repetitions, with 252 animals allocated in 36 cages. The distributions for continuous data used were the exponential, gamma, gaussian, and lognormal. The R Open Source and SAS® University Edition software was used to perform the analysis. The graphical analysis of the traits was performed from the predicted versus observed values, Cumulative Distribution Function (CDF), and skewness-kurtosis. The fits were also evaluated by the Akaike information criterion (AIC), Bayesian information criterion (BIC), Conditional model of adjusted R-Square (), Conditional model of adjusted concordance correlation (), Kolmogorov-Smirnov test (KS), Cramer-von Mises test (CvM), Anderson-Darling test (AD), Watanabe-Akaike Information Criterion (WAIC) and Leave-one-out cross-validation (LOO). All the tests indicated the Gaussian distribution as the most suitable and they excluded the exponential distribution for all the evaluated characteristics.

Bridging Offline Functional Model Carrying Aging-Specific Growth Rate Information and Recombinant Protein Expression: Entropic Extension of Akaike Information Criterion

This study presents a mathematical model of recombinant protein expression, including its development, selection, and fitting results based on seventy fed-batch cultivation experiments from two independent biopharmaceutical sites. To resolve the overfitting feature of the Akaike information criterion, we proposed an entropic extension, which behaves asymptotically like the classical criteria. Estimation of recombinant protein concentration was performed with pseudo-global optimization processes while processing offline recombinant protein concentration samples. We show that functional models including the average age of the cells and the specific growth at induction or the start of product biosynthesis are the best descriptors for datasets. We also proposed introducing a tuning coefficient that would force the modified Akaike information criterion to avoid overfitting when the designer requires fewer model parameters. We expect that a lower number of coefficients would allow the efficient maximization of target microbial products in the upstream section of contract development and manufacturing organization services in the future. Experimental model fitting was accomplished simultaneously for 46 experiments at the first site and 24 fed-batch experiments at the second site. Both locations contained 196 and 131 protein samples, thus giving a total of 327 target product concentration samples derived from the bioreactor medium.

Fitted Copula Statistical Models for Four African and Four Major Stock Markets

The application of copula has become popular in recent years. The use of correlation as a dependence measure has several pitfalls and hence the application of regression prediction model using this correlation may not be an appropriate method. In financial markets, there is often a non-linear dependence between returns. Thus, alternative methods for capturing co-dependency should be considered, such as copula based ones. This paper studies the dependence structure between the four largest African stock markets in terms of market capitalization and other developed stock markets over the period 2003 to 2018 using copula models. The value at risk was used to determine the risk associated with the stock. The ten copula models were fitted to the log returns calculated from the data, two countries at a time of the twenty-eight pairs and examined. The Gumbel copula gives the best fit in terms of log-likelihood values, value of the Akaike information criterion, value of the Bayesian information criterion, value of the consistent Akaike information criterion, value of the corrected Akaike information criterion, value of the Hannan Quinn criterion and p-value of the information matrix equality of White. Estimates of value at risk with probability p for daily returns were computed using the best fitted copula model, from these value at risk, it is seen that SA/FTSE100 have the least risk while EGY/KEN has the highest risk. Prediction is given in terms of correlation and value at risk.