ArticleslgStudy

mathematics

Hirotugu Akaike

Hirotugu Akaike is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Hirotugu Akaike rather than just read about it. In short: Hirotugu Akaike (赤池 弘次, Akaike Hirotsugu; IPA: [akaike çiɾotsɯɡɯ]; November 5, 1927 – August 4, 2009) was a Japanese statistician. In the early 1970s, he formulated the Akaike information criterion (AIC).

Hirotugu Akaike — main illustration
Hirotugu Akaike — illustration

Key takeaways

  • Hirotugu Akaike belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Hirotugu Akaike to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Hirotugu Akaike from memory before moving on to harder problems.

Reference excerpt

Hirotugu Akaike (赤池 弘次, Akaike Hirotsugu; IPA: [akaike çiɾotsɯɡɯ]; November 5, 1927 – August 4, 2009) was a Japanese statistician. In the early 1970s, he formulated the Akaike information criterion (AIC). AIC is now widely used for model selection, which is commonly the most difficult aspect of statistical inference; additionally, AIC is the basis of a paradigm for the foundations of statistics. Akaike also made major contributions to the study of time series. In addition, he had a large role in the general development of statistics in Japan.

Akaike information criterion The Akaike information criterion (AIC) is an estimator of the relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Thus, AIC provides a means for model selection. AIC was first formally described in a research paper by Akaike (1974). As of October 2014, the paper had received more than 14000 citations in the Web of Science: making it the 73rd most-cited research paper of all time. (As of April 2016, the paper had received about 17000 citations.) Nowadays, AIC has become common enough that it is often used without citing Akaike's 1974 paper. Indeed, there are over 170,000 scholarly articles/books that use AIC (as assessed by Google Scholar).

Life and career Hirotugu Akaike was born to a silkworm farmer in Fujinomiya City; he was the youngest of four brothers. In 1957, he married Ayako, with whom he afterward had three daughters; Ayako died (of subarachnoid haemorrhage) in 1983. He later married Mitsuko, who remained his wife for the rest of his life. Akaike graduated with a bachelor's degree from the School of Science at the University of Tokyo, in 1952. He then became a researcher at the Institute of Statistical Mathematics. In 1961, obtained his Doctor of Science, in mathematics, from the University of Tokyo. Afterward, he continued researching at the institute; much of his research pertained to time series, where he made major contributions. From 1986 until 1994, when he retired, he was director general of the institute. In 1988, he founded the Department of Statistical Science at the Graduate University for Advanced Studies; he was chair of the department from the founding until his retirement in 1994. From 1994 until his death, he was an emeritus professor at both the institute and the Graduate University. During his career, Akaike held visiting positions at a number of universities: Princeton (1966–1967), Stanford (1967, 1979), Hawaii (1972), the University of Manchester Institute of Science and Technology (1973), Harvard (Vinton Hayes Senior Fellow in Engineering and Applied Physics, 1976), Wisconsin–Madison (Mathematics Research Center, 1982), and several Japanese universities. He was also president of the Japan Statistical Society. Additionally, he served as a Member of the Science Council of Japan. Akaike died of pneumonia. His obituary in the Journal of the Royal Statistical Society describes him as being "a most gentle person of great intellect, integrity and generosity".

Awards, honors, and related In 1989, Akaike was awarded the Asahi Prize and the Purple Ribbon Medal. In 1996, he was the recipient of the first Japan Statistical Society Prize. In 2000, he was awarded the Gold and Silver Star of the Order of the Sacred Treasure. In 2006, Akaike was awarded the Kyoto Prize; the official citation states that the Prize was for his "Major contribution to statistical science and modeling with the development of the Akaike Information Criterion (AIC)". Akaike was a Fellow at several scientific associations: American Statistical Association, Institute of Electrical and Electronics Engineers, Institute of Mathematical Statistics, Royal Statistical Society, and others. The Akaike Memorial Lecture was founded to honor Akaike's legacy. The Lecture is biennial, and is sponsored jointly by the Institute of Statistical Mathematics and the Japan Statistical Society. On 5 November 2017, Google Doodle commemorated his 90th birthday.

Interviews Findley, David F.; Parzen, Emanuel (1995), "A conversation with Hirotugu Akaike", Statistical Science, 10 (1): 104–117, doi:10.1214/ss/1177010133. Garfield, Eugene (December 21, 1981), "This Week's Citation Classic: Akaike H. A new look at the statistical model identification." (PDF), Current Contents Engineering, Technology, and Applied Sciences, 12 (51): 42, archived from the original (PDF) on November 18, 2017, retrieved March 28, 2006. "Message from Hirotugu Akaike - The 2006 Kyoto Prize Winner" —YouTube video, recorded on November 11, 2006.

Publications

Articles A list of Akaike's research articles (and similar publications) is available on the Hirotugu Akaike Memorial Website, at the Institute of Statistical Mathematics. The list comprises 119 English articles and 52 Japanese articles. What follows here is a selection of English articles. (Many articles in what follows are also in the book Selected Papers of Hirotugu Akaike.)

… excerpt ends here. Continue reading the full article.

Illustrations

Hirotugu Akaike illustration

Worked examples

Example 1 — a first encounter with Hirotugu Akaike

Start with the simplest possible case. Write down what Hirotugu Akaike claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Hirotugu Akaike before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Hirotugu Akaike ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Hirotugu Akaike

In research
Hirotugu Akaike appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Hirotugu Akaike in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Hirotugu Akaike is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1927 births, 2009 deaths, 20th-century Japanese mathematicians, so understanding it makes those chapters shorter.
In everyday life
Look for Hirotugu Akaike outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
Ask Teacher Smith questions about this articleOpens your AI tutor with a question about “Hirotugu Akaike” →

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Hirotugu Akaike in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Hirotugu Akaike means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Hirotugu Akaike out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Hirotugu Akaike in simple terms?

Hirotugu Akaike (赤池 弘次, Akaike Hirotsugu; IPA: [akaike çiɾotsɯɡɯ]; November 5, 1927 – August 4, 2009) was a Japanese statistician. In the early 1970s, he formulated the Akaike information criterion (AIC).

Why does Hirotugu Akaike matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Hirotugu Akaike?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Hirotugu Akaike.

Tags

  • 1927 births
  • 2009 deaths
  • 20th-century Japanese mathematicians
  • 20th-century statisticians
  • 21st-century Japanese mathematicians
  • 21st-century statisticians
  • Fellows of the American Statistical Association
  • Fellows of the IEEE
  • Fellows of the Institute of Mathematical Statistics
  • Fellows of the Royal Statistical Society
  • Japanese statisticians
  • Kyoto laureates in Basic Sciences

Keep exploring